Method for detection of traumatic brain injury

ABSTRACT

The present disclosure describes significant methylation changes in multiple genes and multiple metabolites in body fluid in response to TBI. Gene pathways affected included several known to be involved in neurological and brain function. A large number of good to excellent biomarkers for the detection of TBI was identified. The combination of epigenomic, clinical and metabolomic markers in different combinations overall were highly accurate for the detection of pediatric concussion using Artificial Intelligence-based techniques.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 62/772,441 filed Nov. 28, 2018, entitled “Method for Detection of TBI”, which is incorporated herein by reference in its entirety.

FIELD

The present disclosure provides methods for diagnosing concussion.

BACKGROUND

Traumatic brain injury (TBI) has been reported to occur in 1.6-3.2 million Americans annually [1] and around 70-90% of these injuries are considered to be mild. Given the frequency of failed recognition and under-reporting of mild TBI (mTBI), it is estimated that its true incidence in the US is closer to 3,400,000 cases yearly, three times higher than currently reported [2]. This is an area of significant public health concern, as a marked increase in the emergency room visits has been reported over the last decade (Lumba-Brown A et al. Centers for Disease Control and Prevention guideline on the Diagnosis and Management of mild traumatic brain injury among children, JAMA Pediatr doi:10.1001/jamapediatrics 2018.2853.) The terms “mTBI” and “concussion” are often used interchangeably. While there are now ongoing efforts to distinguish these two conditions, the absence of distinctive symptoms, biomarkers, and consensus diagnostic criteria significantly challenges these efforts [3]. While initially considered a mild, transient disorder with limited long-term consequences, recent data, mainly autopsy based, suggest more significant long-term consequences in mTBI [4].

Sports injuries account for 20% of concussions in children and adults (https://www.cdc.gov/traumaticbraininjury/pdf/bluebook.pdf). Children and teens are among those at greatest risk for concussion. A much larger number of children are engaged in athletic activities than adults [5]. Indeed, one report found that 20.5% of 13-19 year old high-school athletes reported having more than one concussions [6]. In addition, most athletic activities among children are supervised by non-professional volunteers in contrast to college and professional level sports. Lack of medical knowledge significantly limits the ability of such supervising non-professionals to identify concussions when they occur in children, thus magnifying the risk of single impact and recurrent concussion injuries. Not surprisingly, the children themselves may not recognize the injury or its potential significance and have less ability compared to adults, to reliably report when such injuries occur.

Most concussions occur without loss of consciousness. As loss of consciousness is no longer a requirement for diagnosis of concussion, the most conspicuous sign and symptom has been removed from the diagnostic requirements, thereby increasing the chances of failed recognition in children. Together these findings significantly elevate the likely frequency of pediatric concussion.

Pediatric concussion presents a unique challenge and the health consequences may be even greater than concussion in older individuals. Published evidence suggests that when controlled for age, neurocognitive performance in concussed children <7 years old was worse than in older children [7]. Many explanations have been offered to try to understand the increased vulnerability of children to concussive blows. These include immaturity of the pediatric brain, relatively larger head size, thinner and thus less protective cranial bones and larger subarachnoid space which allows greater mobility of the brain on impact [5]. Incomplete myelination may put the pediatric brain at greater risk for shear injury [8].

Children with mTBI have more extensive cerebral edema and more metabolic perturbations than compared to adults [9]. Finally, children may be at increased risk for severe complications of recurrent concussion [10] and younger children <10 years old are more likely to display adverse behavioral outcomes compared to older children [11].

Given the unique vulnerabilities of children summarized above, biomarkers would be particularly beneficial in this population for accurate assessment and diagnosis, and appropriate management of concussion [12]. Despite an exponential increase in the number of concussion biomarkers that are being evaluated, there is currently no specific and sensitive biomarker for concussion that is available for clinical use at the bedside.

SUMMARY

Measuring methylation levels of cytosine (‘CpG’) loci throughout the genome, a total of 412 CpG sites, each associated with a separate gene, in which there was significant methylation changes associated with concussion compared to unaffected controls, in leucocyte DNA were found. There were a total of 119 methylation markers with good individual diagnostic accuracy (AUC ≥0.80-0.89) and four with excellent individual diagnostic accuracy (AUC ≥0.90-1.00) for the detection of concussion. The percentage difference in CpG methylation between pediatric concussion and controls was >10% in many of the methylation sites suggesting biological significance i.e. methylation level changes are significant enough to affect gene transcription. Pathway analysis using the differentially methylated genes identified several biologically important neuronal and brain pathways that were perturbed including those associated with: impaired brain function, memory, neurotransmission, intellectual disability, cognitive impairment, behavioral change and associated disorders including Alzheimer's disease, severe epileptic encephalopathy. Targeted metabolomic analysis (Nuclear Magnetic resonance-“NMR” and Liquid chromatography-Mass Spectrometry-Mass Spectrometry (LC-MS-MS)) was also performed on serum of the cases and controls undergoing epigenomic evaluation. Using Deep Learning (DL)/artificial intelligence (AI) and other machine learning (ML) AI (and also conventional logistic regression approaches), the combination of epigenomic, clinical and metabolomic markers was found to be highly accurate for the detection of concussion including mTBI. Children and adolescents are at high risk for concussion and therefore were used in this study. The findings significance herein and applications are in no way limited to children and adolescents however.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B. Display Receiver operating characteristic (ROC) curve (graphic demonstration of predictive accuracy) of four specific CpG loci for the prediction of Pediatric concussion. A total of 449 CpG sites in 412 differentially-methylated genes that have an area under the ROC curve ≥0.75 were identified. An AUC ≥0.75 suggest that a marker could potentially have clinical utility. At each of these differentially methylated cytosine (CpG) locus, the False Detection Rate (FDR) p-value for the methylation difference between pediatric concussion cases and controls was <0.0001 and thus highly statistically significant. The most important individual CpG locus (and associated genes) based on level of methylation change in mTBI compared to unaffected controls included markers (chr 14; Cg27634876-PTGDR), (chr 8; Cg02812947-ADCY8), (chr 11; Cg03167496-BDNF) {chr 1; Cg02147055-PTPRC)—(where the chromosome; CpG locus-associated gene symbol provided). AUC: Area Under the Receiver Operating Characteristics Curve; 95% CI: 95% Confidence Interval. Lower and upper Confidence Intervals are given in parentheses.

FIG. 2. Pathways analysis displaying gene and gene-pathways that appear to be epigenetically dysregulated in mTBI.

DETAILED DESCRIPTION

Concussion, also known as minor head trauma or mild traumatic brain injury (mTBI) is the most common type of traumatic brain injury. Guidelines now recommend the use of the term mild traumatic brain injury. Several organizations including the World Health Organization (WHO) US Centers for Disease Control and Prevention (CDC) define mTBI as “an acute brain injury resulting from mechanical energy to the head from external physical forces.” One or more of the following findings are required to make the diagnosis: 1. confusion, disorientation, loss of consciousness for 30 minutes or less, post traumatic amnesia for less than 24 hours and/or other transient neurologic abnormalities such as focal signs, symptoms or seizure. 2. Glasgow Coma Scale score of 13-15 after 30 minutes post-injury or later upon presentation for healthcare (Carroll L J, Cassidy J D, Holm L, et al. WHO Collaborating Center Task Force on Mild Traumatic Brain Injury. Methodological Issues and research recommendations for mild traumatic brain injury. J Rehab Med 2004; 43(suppl.):113-25.) Most concussions are unrecognized or do not present for medical attention. While most concussion resolve within six weeks, post-concussion syndrome can include physical, cognitive, and emotional problems. There is currently intense scientific interest in understanding the biological mechanisms of and in the development of biomarkers for the detection and monitoring of this common disorder. The pediatric period is one of heightened risk both in terms of frequency and also the susceptibility of the developing brain to perceived minor trauma.

Individuals at high risk for concussion extend across the general population. The highest rates are in young children (0-4 years), who are unable to provide any significant history. Older adolescents (age 15-19 years) and the elderly have high rates of TBI and concussion also. From the point of view of ethnicity, African Americans have the highest rates of TBI followed by whites. There is also a strong correlation between TBI and with alcohol use and abuse, which puts college students at elevated risk. Further, the relationship between TBI and alcohol use is heavily correlated with the occurrence of motor vehicle accidents. Falls are the most common cause of TBI, and this partly accounts for the high rates of TBI in young children and elderly individuals. Sports and recreational activities are a major cause of mTBI. The impact on children and adolescents is enormous. Approximately 30-45 million children and adolescents in the USA are believed to participate in organized sport each year. Approximately 7.6 million adolescents are engaged in high school sports. The risk of concussion is highest in contact sports. With 1.1 million high school students involved in American football there is a high level of risk for high school athletes in general and in particular in those playing football. These numbers are significantly larger than the number of college and professional athletes! Approximately 36% of college athletes report concussion, while 25% of high school athletes report concussion with 20% reporting recurrent concussions. According to the CDC there are up to 3.8 million sports related concussions per year in the USA. The American College of Sports Medicine estimates that 85% of sports related concussions are under-diagnosed.

Individuals in the military are at high risk of combat related TBI. The Department of Defense reported a total of approximately 300,000 TBI between 2000-2013 of these 80% were reported to be concussions.

A high percentage of the USA population is at risk for concussion and as noted the estimates of concussion frequency are likely to be gross underestimates, this is in large part due to the fact that objective tests or biomarkers do not currently exist. Currently the diagnosis of concussion is based on neuropsychological tests, such as Glasgow Coma Scale Score. Findings may be lacking on physical exam. Neuroimaging techniques such as CT and MRI are also used however the CT has low sensitivity for the milder forms of brain injury and MRI is too expensive for routine assessment. Relatively newer imaging techniques such as functional MRI, diffusion tensor imaging (DTI) and magnetic resonance spectroscopy and PET scanning are currently being evaluated. The cost and lack of uniform expertise with these technologies across hospitals remain a concern however.

During the early post traumatic period symptoms such as decline of cognition (difficulty concentrating, distractedness, forgetfulness), headaches, dizziness, fatigue, and depression can develop. In a percentage of cases, these symptoms can persist for many months. However, longer term consequences such as post traumatic sleep disorder develops in a significant minority of cases (˜14%). Recurrent concussive episodes are now thought to increase the risk of severe long-term brain disorders such as chronic traumatic encephalopathy (CTE). The pediatric population is at higher risk for symptoms and complications from concussion. This is thought to be due to the immaturity of the brain, relatively larger head size and therefore a bigger target for injury and the softer skull, thus providing less protection against outside forces. A recent publication documented that among college and professional football players, those who started playing before age 12 years were at higher risk of developing subsequent Chronic Traumatic encephalopathy (CTE) emphasizing the unique susceptibility to trauma of children's the brain. Finally, the CDC estimated in a report to congress in 2003 that the annual cost of mild TBI in the US is approximately $16.7 billion.

Importance of Concussion/TBI Biomarkers.

The FDA and similar international organizations have prioritized the development of biomarkers for TBI and concussion. Biomarker development is an important part of the Critical Pathway Initiative of the FDA.

The main function of the ideal concussion biomarker would be for the detection of this under-diagnosed disorder. Additional desirable benefits could include the ability to measure concussion severity, monitor disease progression, long term prognosis and brain response to therapy. Future biomarkers could add very significant value by indicating the particular anatomical area(s) of the brain that have been injured.

Existing Concussion/TBI Biomarkers.

Some of the best studied biomarkers currently include s100β, Glial Fibrillary Acidic Protein (GFAP), Neuronal specific enolase (NSE), Tau Protein and others. A more extensive list can be obtained at (CNS Trauma Biomarkers and surrogate endpoints pipeline from Bench to bedside. In Kobeissy F N, editors, Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. Boca Raton (Fla.). CRC Press/Taylor and Francis 2015).

A further important benefit of biomarkers is to elucidate the mechanisms of ongoing brain damage after the initial trauma (e.g. oxidative stress, inflammation, biochemical abnormalities etc.) and thus generate the scientific basis for the development of novel pharmaceuticals and other treatment agents. Despite the significant potential benefits of biomarkers these have not been realized. The current recommendation is that outside of a research setting, biomarkers should not be used for the evaluation of mTBI in children (Lumba-Brown A et al. Centers for Disease Control and Prevention guideline on the Diagnosis and Management of mild traumatic brain injury among children, JAMA Pediatr doi:10.1001/jamapediatrics 2018.2853).

While genetic factors are generally not causative for the initial trauma, there is still the potential that genetic factors could have a significant role in the subsequent inflammatory response that contributes so significantly to ongoing brain damage following the initial trauma. Polymorphisms of many genes have been linked to outcomes in TBI. This have been comprehensively reviewed elsewhere. [13] Such genes include but are not limited to TNF-α, IL-1, IL-6 and APOE 4. However, these findings have not been uniformly confirmed, and there is even less genetic data available on TBI in children. The APOE 4 allele has been linked to poor global outcomes in concussion in children [14]. A genome-wide association study (GWAS) study yielded four qualified articles which cumulatively confirmed an association between the APOE gene allele and neurologic outcomes in TBI in children [15].

There is paucity of data about the epigenetics of concussion. Animal experimental data reveals an influence of epigenetic changes, including histone modification and DNA hypomethylation, in the brain's response to TBI [16, 17]. Decreases in plasma methionine levels have been reported after mTBI in humans [18]. Methionine, is an essential amino acid, serves as a substrate for s-adenosyl methionine (SAM). SAM is the major methyl donor for methylation reactions including DNA methylation, a key epigenetic mechanism. A decrease in SAM production was observed in mild traumatic brain injury (mTBI), as well as decreases in serum choline and betaine concentrations. Both choline and betaine are also methyl donors involved in DNA methylation pathways.

The monumental challenge in studying TBI is the inaccessibility of the brain for direct study in clinical subjects. A study by Petrone et al. [19] found that an early response to mTBI in (non-pediatric) human subjects was a change in peripheral blood neutrophil and lymphocyte counts. More interestingly, they demonstrated significant changes in the expression level of many immune-related genes in the blood of mTBI patients, including MMP9, LY96, CCR, 7ARG1, and S100a12. Good diagnostic accuracy for mTBI detection were reported using blood mRNA expression levels of these genes. The purpose of this study and application is to identify blood-based biomarkers for accurate detection of mTBI in patients including adults and pediatric patients, as no such markers are available to date.

Metabolomics refers to the quantitation and identification of the small molecules which are the substrates and by-products of cellular biochemical reactions. Global assessment of endogenous and exogenous metabolites from the cells, tissues or biofluids is performed (Wishart O S. Chapter 3: Small molecules and disease. PLoS Comput Biol. 2012; 8(12: e1002805). Metabolomics is down-stream of genomics, proteomics and transcriptomics and most closely correlates with disease phenotype. It involves the comprehensive, simultaneous and systematic profiling of metabolite concentrations, which is sensitive to disease phenotype. It holds significant promise both for the generation of biomarkers for disease detection and for elucidating the pathogenesis of disease. Limited data currently exists on the use of metabolomics in traumatic brain injury but animal data suggest that metabolomic markers may have significant diagnostic accuracy for traumatic brain injury (Bahado-Singh et al., (2016) Serum Metabolomic Markers for Traumatic Brain Injury: A Mouse Model. Metabolomics 12:100. https://doi.or/10.1007/s11306-016-1044-3: Bahado-Singh et al., (2016) Identification of candidate biomarkers of brain damage in a mouse model of closed head injury: a metabolomic pilot study. Metabolomics. 12:42. https://doi.org/10.1007/s11306-016-0957-1).

A recent metabolomics study (REF: Daley_M. Qekaban, G., Bartha, R. et al. Metabolomics (2016) 12:185. https://doi.org/10.10071s11306-0 16-1131-5) including adolescent athletes was able to achieve the 92% diagnostic accuracy in the detection of concussion. To this date this is the only metabolomics study that was performed in the identification of pediatric concussion biomarkers. This data however was generated from children at the border of adulthood and strictly related to trauma resulting from athletic pursuits which is likely to be more severe than brain trauma sustained from other head trauma sustained during the course of routine childhood activities.

Artificial Intelligence (AI) represents a discipline in computer sciences which focuses on the development of systems for the performance of intellectual tasks that are normally requires human cognition (Chartrand G, Cheng P M, Vorontsov E, et al. deep Learning: A primer for Radiologists RadioGraphics 2017; 37:2113-31). Machine Learning (ML) is a branch of AI in which computers are trained to perform tasks based on the computers experiential learning rather than by explicit programming by humans. Deep Learning is the latest and a rapidly developing branch of AI. Deep Learning is a class of machine learning in which the number of layers of neural networks is significantly expanded i.e. greater ‘neuronal’ complexity and thus expanding computational power. It has been shown to further improve performance in important current applications such as image and speech recognition. It appears to have a greater capacity to handle and interpret the torrent of data being generated by systems biology analysis. and preliminary data would suggest that it is ideally suited for the complexities of epigenomic and metabolomic data analysis. Other examples of AI algorithms include Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Prediction of Analysis for Microarrays (PAM), and Generalized Linear Model (GLM).

The term “epigenetics” can be used to describe the interaction between genes and the environment. These interactions do not result in changes to the genome itself (no nucleotide sequence changes) but still account for variations in phenotypic expression. Epigenetic modifications are a major mechanism by which injury and destructive prenatal environmental factors can lead to long-term disturbances of brain development, such as cognitive, motor, or behavioral impairments. During the acute and secondary phases of brain injury there is substantial loss of acetylation and methylation tags and considerable variation in microRNA expression. MicroRNA are short non-coding RNAs that control gene expression and are also known to exert significant influence on DNA methylation. Reduced acetylation, another epigenetic mechanism, is associated with cognitive decline, which is accelerated after brain injury. Changes to epigenetic processes might be particularly relevant for white matter injury. Epigenetic dysregulation occurs with important risk factors (for example, age, alcohol usage, etc.) for TBI including mTBI.

Epigenetics is defined as heritable (i.e. passed onto offspring) changes in gene expression of cells that are not primarily due to mutations or changes in the sequence of nucleotides (adenine, thiamine, guanine, and cytosine) in the genes. Epigenetics is a reversible regulation of gene expression by several potential mechanisms. One such mechanism which is the most extensively studied is DNA methylation. Other mechanisms include changes in the 3-dimensional structure of the DNA, histone protein modification, and micro-RNA inhibitory activity. The epigenetic mechanisms are known to be extensively inter-related.

The present disclosure describes the use of epigenomic and metabolomic markers and Artificial Intelligence analytic techniques for accurate detection of pediatric concussion.

Epigenomic Analysis.

Using the Illumina HumanMethylation450 BeadChip assay, methylation levels of CpG sites across the genome were examined in 18 pediatric concussion cases and compared to 18 of unaffected healthy matched controls. Pathway analysis was performed using Ingenuity pathway analysis to elucidate the mechanism of the disorder. In addition, the diagnostic accuracy of epigenomic markers for the detection of concussion was determined. Area under the receiver operating characteristics (AUC) curves and 95% CI and FDR p-values were calculated for the detection of concussion.

Metabolomic Analysis.

In addition to epigenomic analysis, ¹H-NMR (proton based Nuclear magnetic resonance) spectroscopy and Liquid chromatography-Mass spectrometry-Mass spectrometry (LC-MS-MS)—based metabolomic analysis was performed on the serum of the study patients. Clinical and demographic data were obtained for each patient. We report on a subgroup of cases and controls that had both epigenomic and metabolomic analysis to demonstrate the value of combined omics for the detection of concussion.

Several different Artificial Intelligence (AI) techniques including Deep Learning (DL), the newest form of AI, were used to predict concussion using i) epigenetic i.e. DNA methylation markers ii) metabolomic iii) clinical and demographic markers and finally iv) different combination of omics, clinical and demographic markers.

The present disclosure describes the identification of 412 discrete CpG loci, each associated with a distinct gene, in which there was statistically significant methylation changes associated with concussion. There was a total of 119 methylation markers with good individual diagnostic accuracy (AUC ≥0.80-0.89) and four with excellent individual diagnostic accuracy (AUC ≥0.90-1.00) for the detection of concussion. The CpG methylation differences between pediatric concussion and controls was 210% in many of the methylation sites suggesting biological significance i.e. an effect on gene expression. Ingenuity molecular Pathway analysis based on the affected genes identified several biologically important neurological (gene) pathways that were perturbed including those associated with: impaired brain function, memory, neurotransmission, intellectual disability, cognitive impairment, behavioral change and associated disorders including Alzheimer's disease, severe epileptic encephalopathy. Epigenomic markers by themselves were highly accurate for the detection of concussion. When these were combined with clinical predictors the accuracy was only minimally higher (but not statistically significantly so) than the use of epigenomic markers alone. Combinations of epigenomic, clinical and metabolomic markers were evaluated in differently performing subsets of epigenomic markers. The metabolomic markers include metabolites or small molecules. The addition of metabolomic to epigenetic markers slightly lowered predictive accuracy from epigenomic markers by themselves. Using all three groups of markers (epigenomic, clinical plus metabolomic markers) did not improve accuracy over combined epigenetic plus clinical markers combined, but slightly improved performance over epigenetic markers alone.

The present disclosure confirms highly significant differences in the percentage methylation of cytosine nucleotides in leucocyte DNA throughout the genome in individuals with mTBI versus normal groups using a widely available commercial bisulfite-based assay for distinguishing methylated from unmethylated cytosine. Cytosine loci analyzed were not limited to CpG islands or to specific genes but included cytosine loci outside of CpG islands and outside of genes. For the purposes of this particular disclosure, cytosine loci associated with known genes were reported. Significant differences in cytosine methylation loci throughout the genome were observed between TBI patients and unaffected controls. The combination of cytosine loci can be used to accurately predict TBI and particularly mTBI or concussion which is the most common form of TBI and which frequently fails to be diagnosed.

Particular aspects provide panels of known and identifiable cytosine loci throughout the genome whose methylation levels (expressed as percentages) is useful for distinguishing TBI from normal cases.

Additional aspects describe the capability of combining other recognized TBI clinical tests and novel metabolomics markers combined with cytosine methylation for the diagnosis of TBI. Multiple individual cytosine (CpG) loci demonstrate highly significant differences in the degree of their methylation in TBI versus normal cases (FDR q-values <0.05 to 1.0×10³²).

TBI is classified based on the severity of the injury, the pathological features of the injury, and the mechanism (causative forces) of the injury. Severity of TBI could be mild, moderate and severe depending on the extent of damage to the brain, patients level of consciousness and reactions to stimuli. Pathological features can include lesions within the skull and outside of the brain or within the brain tissue. The injury can also be focal, confined to a specific area, or diffuse, distributed to a general area. Causes of the TBI can include falls, violence, transportation accidents, and sports.

Cytosine refers to one of a group of four building blocks “nucleotides” from which DNA is constructed. The chemical structure of cytosine is in the form of a pyrimidine ring. Apart from cytosine, the other nucleotides or building blocks found in DNA are thiamine, adenine, and guanosine.

The term methylation refers to the enzymatic addition of a “methyl group” or single carbon atom to position #5 of the pyrimidine ring of cytosine which leads to the conversion of cytosine to 5-methyl-cytosine. The methylation of cytosine as described is accomplished by the actions of a family of enzymes named DNA methyltransferases (DNMTs). The 5-methyl-cytosine when formed is prone to mutation or the chemical transformation of the original cytosine to form thymine. Five-methyl-cytosines account for about 1% of the nucleotide bases overall in the normal genome.

The term hypermethylation refers to increased frequency or percentage methylation at a particular cytosine locus when specimens from an individual or group of interest is compared to a normal or control group.

Cytosine is usually paired with guanosine another nucleotide in a linear sequence along the single DNA strand to form CpG pairs. “CpG” refers to a cytosine-phosphate-guanosine chemical bond in which phosphate binds the two nucleotides together. In mammals, in approximately 70-80% of these CpG pairs the cytosine is methylated (Chatterjee R, Vinson C. Biochemica et Biophisica Acta 2012; 1819:763-70). The term “CpG island” refers to regions in the genome with high concentration of CG dinucleotide pairs or CpG sites. “CpG islands” are often found close to genes in mammalian DNA. The length of DNA occupied by the CpG island is usually 300-3000 base pairs. The CG cluster is on the same single strand of DNA. The CpG island is defined by various criteria including i) the length of recurrent CG dinucleotide pairs occupying at least 200 bp of DNA and ii) a CG content of the segment of at least 50% along with the fact that the observed/expected CpG ratio should be greater than 60%. In humans about 70% of the promoter regions of genes have high CG content. The CG dinucleotide pairs may exist elsewhere in the gene or outside of a gene and not know to be associated with a particular gene.

Approximately 40% of the promoter region (region of the gene which controls its transcription or activation) of mammalian genes have associated CpG islands and three quarters of these promoter-regions have high CpG concentrations. Overall in most CpG sites scattered throughout the DNA the cytosine nucleotide is methylated. In contrast in the, CpG sites located in the CpG islands of promoter regions of genes, the cytosine is unmethylated suggesting a role of methylation status of cytosine in CpG Islands in gene transcriptional activity.

The methylation of cytosines associated with or located in a gene is classically associated with suppression of gene transcription. In some genes however, increased methylation has the opposite effect and results in activation or increased transcription of a gene. One potential mechanism explaining the latter phenomenon is that methylation of cytosine could potentially inhibit the binding of gene suppressor elements thus releasing the gene from inhibition. Epigenetic modification, including DNA methylation, is the mechanism by which for example cells which contain identical DNA and genes experience activation of different genes and result in the differentiation into unique tissues e.g. heart or intestines.

The receiver operating characteristics (ROC) curve is a graph plotting sensitivity—defined in this setting as the percentage of TBI cases with a positive test or abnormal cytosine methylation levels at a particular cytosine locus on the Y axis and false positive rate (1—specificity or 100% —specificity when the latter is expressed as a percentage)—i.e. the number of normal non-TBI cases with abnormal cytosine methylation at the same locus—on the X-axis. Specificity is defined as the percentage of normal cases with normal methylation levels at the locus of interest or a negative test. False positive rate refers to the percentage of normal individuals falsely found to have a positive test (i.e. abnormal methylation levels); it can be calculated as 100-specificity (%) or expressed as a decimal format [1-specificity (expressed as a decimal point)].

The area under the ROC curves (AUC) indicates the accuracy of the test in identifying normal from abnormal cases³⁷.

The AUC is the area under the ROC plot from the curve to the diagonal line from the point of intersection of the X- and Y-axes and with an angle of incline of 45°. The higher the area under receiver operating characteristics (ROC) curve the greater is the accuracy of the test in predicting the condition of interest. An area under the ROC=1.0 indicates a perfect test, which is positive (abnormal) in all cases with the disorder and negative in all normal cases (without the disorder). Methylation assay refers to an assay, a large number of which are commercially available, for determining the level of methylation at a particular cytosine in the genome. In this particular context, we are using this approach to distinguish the level of methylation in affected cases (mTBI) compared to unaffected controls.

Methylation Assays.

Several quantitative methylation assays are available. These include COBRA™ which uses methylation sensitive restriction endonuclease, gel electrophoresis and detection based on labeled hybridization probes. Another available technique is the Methylation Specific PCR (MSP) for amplification of DNA segments of interest. This is performed after sodium ‘bisulfite’ conversion of cytosine using methylation sensitive probes. MethyLight™, a quantitative methylation assay-based uses fluorescence based PCR. Another method used is the Quantitative Methylation (QM™) assay, which combines PCR amplification with fluorescent probes designed to bind to putative methylation sites. Ms-SNuPE™ is a quantitative technique for determining differences in methylation levels in CpG sites. As with other techniques bisulfite treatment is first performed leading to the conversion of unmethylated cytosine to uracil while methyl cytosine is unaffected. PCR primers specific for bisulfite converted DNA is used to amplify the target sequence of interest. The amplified PCR product is isolated and used to quantitate the methylation status of the CpG site of interest. The preferred method of measurement of cytosine methylation is the Illumina method.

Illumina Method.

For DNA methylation assay the Illumina Infinium® Human Methylation 450 Beadchip assay was used for genome wide quantitative methylation profiling. Briefly genomic DNA is extracted from cells in this case archived blood spot, for which the original source of the DNA is white blood cells. Using techniques widely known in the trade, the genomic DNA is isolated using commercial kits. Proteins and other contaminants were removed from the DNA using proteinase K. The DNA is removed from the solution using available methods such as organic extraction, salting out or binding the DNA to a solid phase support.

Bisulfite Conversion.

As described in the Infiniumrr Assay Methylation Protocol Guide, DNA is treated with sodium bisulfite which converts unmethylated cytosine to uracil, while the methylated cytosine remains unchanged. The bisulfite converted DNA is then denatured and neutralized. The denatured DNA is then amplified. The whole genome application process increases the amount of DNA by up to several thousand-fold. The next step uses enzymatic means to fragment the DNA. The fragmented DNA is next precipitated using isopropanol and separated by centrifugation. The separated DNA is next suspended in a hybridization buffer. The fragmented DNA is then hybridized to beads that have been covalently limited to 50mer nucleotide segments at a locus specific to the cytosine nucleotide of interest in the genome. There is a total of over 500,000 bead types specifically designed to anneal to the locus where the particular cytosine is located. The beads are bound to silicon-based arrays. There are two bead types designed for each locus, one bead type represents a probe that is designed to match to the methylated locus at which the cytosine nucleotide will remain unchanged. The other bead type corresponds to an initially unmethylated cytosine which after bisulfite treatment is converted to a thiamine nucleotide. Unhybridized (not annealed to the beads) DNA is washed away leaving only DNA segments bound to the appropriate bead and containing the cytosine of interest. The bead bound oligomer, after annealing to the corresponding patient DNA sequence, then undergoes single base extension with fluorescently labeled nucleotide using the ‘overhang’ beyond the cytosine of interest in the patient DNA sequence as the template for extension.

If the cytosine of interest is unmethylated then it will match perfectly with the unmethylated or “U” bead probe. This enables single base extensions with fluorescent labeled nucleotide probes and generate fluorescent signals for that bead probe that can be read in an automated fashion. If the cytosine is methylated, single base mismatch will occur with the “U” bead probe oligomer. No further nucleotide extension on the bead oligomer occurs however thus preventing incorporation of the fluorescent tagged nucleotides on the bead. This will lead to low fluorescent signal form the bead “U” bead. The reverse will happen on the “M” or methylated bead probe.

Laser is used to stimulate the fluorophore bound to the single base used for the sequence extension. The level of methylation at each cytosine locus is determined by the intensity of the fluorescence from the methylated compared to the unmethylated bead. Cytosine methylation level is expressed as “β” which is the ratio of the methylated bead probe signal to total signal intensity at that cytosine locus. These techniques for determining cytosine methylation have been previously described and are widely available for commercial use.

The present disclosure describes the use of a commercially available methylation technique to cover up to 99% Ref Seq genes involving approximately 16,000 genes and 450,000 cytosine nucleotides down to the single nucleotide level, throughout the genome (Infinium Human Methylation 450 Beach Chip Kit). The frequency of cytosine methylation at single nucleotides in a group of TBI cases compared to controls is used to estimate the risk or probability of being diagnosed with TBI. The cytosine nucleotides analyzed using this technique included cytosines within CpG islands and those at further distances outside of the CpG islands i.e. located in “CpG shores” and “CpG shelves” and even more distantly located from the island so called “CpG seas”.

Identification of Specific Cytosine Nucleotides.

Reliable identification of specific cytosine loci distributed throughout the genome has been detailed (Illumnia) in the document: “CpG Loci Identification. A guide to Illumina's method for unambiguous CpG loci identification and tracking for the GoldenGate®) and Infinium™ assays for Methylation.” A brief summary follows. Illumina has developed a unique CpG locus identifier that designates cytosine loci based on the actual or contextual sequence of nucleotides in which the cytosine is located. It uses a similar strategy as used by NCBI's re SNP IPS (rs#) and is based on the sequence flanking the cytosine of interest. Thus, a unique CpG locus cluster ID number is assigned to each of the cytosine undergoing evaluation. The system is reported to be consistent and will not be affected by changes in public databases and genome assemblies. Flanking sequences of 60 bases 5′ and 3′ to the CG locus (i.e. a total of 122 base sequences) is used to identify the locus. Thus, a unique “CpG cluster number” or cg# is assigned to the sequence of 122 bp which contains the CpG of interest. The cg# is based on Build 37 of the human genome (NCBI37). Accordingly, only if the 122 bp in the CpG duster is identical is there a risk of a locus being assigned the same number and being located in more than one position in the genome. Three separate criteria are utilized to track individual CpG locus based on this unique ID system. Chromosome number, genomic coordinate and genome build. The lesser of the two coordinates “C” or “G” in CpG is used in the unique CG loci identification. The CG locus is also designated in relation to the first ‘unambiguous” pair of nucleotides containing either an ‘A’ (adenine) to ‘T’ (thiamine). If one of these nucleotides is 5′ to the CG then the arrangement is designated TOP and if such a nucleotide is 3′ it is designate BOT.

In addition, the forward or reverse DNA strand is indicated as being the location of the cytosine being evaluated. The assumption is made that methylation status of cytosine bases within the specific chromosome region is synchronized⁴¹.

Description of the Method.

A total of 18 cases of TBI, along with a total of 18 controls underwent epigenetic analysis. Control cases were normal patients at the time of chart review and at patient reporting and with no known or suspected brain injury. TBI patients as a single group was compared to unaffected controls.

In embodiments, the present disclosure describes a method for diagnosing TBI based on measurement of frequency or percentage methylation of cytosine nucleotides in various identified loci in a DNA sample. In embodiment, the DNA sample can be obtained from a biological sample of a patient in need thereof. The method includes obtaining a biological sample from a patient; extracting DNA from the sample; assaying the sample to determine the percentage methylation of cytosine at loci throughout genome; comparing the cytosine methylation level of the patient to a control; and calculating the individual risk of being diagnosed with TBI based on the cytosine methylation level at different sites throughout the genome. In embodiments, the patients could be adults and the control could be a well characterized group of normal (healthy) people and/or well characterized population of TBI patients. In embodiments, the patient could be a pediatric patient. The pediatric patient can be less than about 19 years old, about 15 to 19 years old, less than about 15 years old, about 10 to 15 years old, less than 10 years old, about 5 to 10 years old, less about 4 years old, about 1 to 4 years old, or less than one year old. The control could be a well characterized group of normal (healthy) children of less than about 19 years old and/or well characterized population of TBI pediatric patients. The well characterized group of normal people or TBI patients may include one or more normal people or TBI patients or may include a population of normal people or TBI patients. The control group of normal people or TBI patients could be children of less than 19 years of age or adults of more than 19 years of age.

DNA Extraction from Blood-Spot.

DNA was obtained from blood draw or venipuncture. DNA extraction can similarly be obtained from a fingerstick leading to a blood spot on filter paper and performed as described in the EZ1® DNA Investigator Handbook, Sample and Assay Technologies, QIAGEN 4^(th) Edition, April 2009. A brief summary of the DNA extraction method is provided. Two 6 mm diameter circles (or four 3 mm diameter circles) are punched out of a dried blood spot stored on filter paper and used for DNA extraction. The circle contains DNA from white blood cells from approximately 5 μL of whole blood. The circles are transferred to a 2 ml sample tube.

A total of 190 μL of diluted buffer G2 (G2 buffer distilled water in 1:1 ratio) are used to elute DNA from the filter paper. Additional buffer is added until residual sample volume in the tube is 190 1 L since filter paper will absorb a certain volume of the buffer. Ten μL of proteinase K is added and the mixture is vortexed for 10 s and quick spun. The mixture is then incubated at 56° C. for 15 minutes at 900 rpm. Further incubation at 95° C. for 5 minutes at 900 rpm is performed to increase the yield of DNA from the filter paper. Quick spin is then performed. The sample is then run on EZ1Advanced (Trace, Tip-Dance) protocol as described. The protocol is designed for isolation of total DNA from the mixture. Elution tubes containing purified DNA in 50 μL of water is now available for further analysis.

Infinium DNA Methylation Assay.

Methylation Analysis-Illumina's Infinium Human Methylation 450 Bead Chip system was used for genome-wide methylation analysis. DNA (500 ng) was subjected to bisulfite conversion to deaminate unmethylated cytosines to uracil with the EZ-96 Methylation Kit (Zymo Research) using the standard protocol for the Infinium assay. The DNA is enzymatically fragmented and hybridized to the Illumina BeadChips. BeadChips contain locus-specific oligomers and are in pairs, one specific for the methylated cytosine locus and the other for the unmethylated locus. A single base extension is performed to incorporate a biotin-labeled ddNTP. After fluorescent staining and washing, the BeadChip is scanned and the methylation status of each locus is determined using BeadStudio software (Illumina). Experimental quality was assessed using the Controls Dashboard that has sample-dependent and sample-independent controls target removal, staining, hybridization, extension, bisulfite conversion, specificity, negative control, and non-polymorphic control. The methylation status is the ratio of the methylated probe signal relative to the sum of methylated and unmethylated probes. The resulting ratio indicates whether a locus is unmethylated (0) or fully methylated. Differentially methylated sites are determined using the Illumina Custom Model and filtered according to p-value using 0.05 as a cutoff.

Illumina's Infinium HumanMethylation450 BeadChip system, an updated assay method that covers CpG sites (containing cytosine) in the promoter region of more genes, i.e., approximately 16,880 genes. In addition other cytosine loci throughout the genome and outside of genes, and within or outside of CpG islands are represented in this assay.

Cytosine Methylation for the Diagnosing TBI Using ROC Curve.

To determine the accuracy of the methylation level of a particular cytosine locus for TBI prediction, different threshold levels of methylation e.g. 10%, ≤20%, ≤30%, ≤40% etc. at the site was used to calculate sensitivity and specificity for TBI diagnosis. Thus, for example using ≤10% methylation at a particular cg locus, cases with methylation levels above this threshold would be considered to have a positive test and those with lower than this threshold are interpreted as a negative methylation test. The percentage of TBI cases with a positive test in this example 10% methylation at this particular cytosine locus would be equal to the sensitivity of the test. The percentage of normal non-TBI cases with cytosine methylation levels of <10% at this locus would be considered the specificity of the test. False positive rate is here defined as the number of normal cases with a (falsely) abnormal test result and sensitivity is defined as the number of TBI cases with (correctly) abnormal test result e.g. the level of methylation 10% at this particular cg location. A series of threshold methylation values are evaluated e.g. ≤ 1/10, ≤ 1/20, ≤ 1/30 etc., and used to generate a series of paired sensitivity and false positive values for each locus. A receiver operating characteristic (ROC) curve which is a plot of data points with sensitivity values on the Y-axis and false positivity rate on the X-axis is generated. This approach can be used to generate ROC curves for each individual cytosine locus that displays significant methylation differences between cases and TBI groups. In this instance the computer program ROCR package-version 3.4 ((https://CRAN.R-project.or/package=ROCR) was used to generate the area under the ROC curves.

Standard statistical testing using p-values to express the probability that the observed difference between cytosine methylation at a given locus between TBI and control DNA specimens were performed.

More stringent testing of statistical significance using False Discovery Rate (FDR) for multiple comparison was also performed. The FDR gives the probability that positive results were due to chance when multiple hypothesis testing is performed using multiple comparisons.

In embodiments, using the Illumina Infinium Assays for whole genome methylation studies, significant differences in the frequency (level or percentage) of methylation of specific cytosine nucleotides associated with particular genes were demonstrated in the TBI group when compared to a normal group. The differences in cytosine methylation levels are highly significant and of sufficient magnitude to accurately distinguish the TBI from the normal group. Thus, the methods described herein can be used as a test to screen for TBI cases among a mixed population with TBI and normal cases.

The degree of methylation of cytosines could potentially vary based on individual factors (diet, race, age, gender, medications, toxins, environmental exposures, other concurrent medical disorders and so on). Overall, despite these potential sources of variability, whole genome cytosine methylation studies identified specific sites within (and outside of) certain genes and could distinguish and therefore could serve as a useful blood screening test for identification of groups of individuals predisposed to or at increased risk for being diagnosed with TBI compared to normal cases.

Since cells, with few exceptions (mature red blood cells and mature platelets), contain nuclei and therefore DNA, the methods described herein can be used to screen for TBI using DNA from any cells with the exception of the two named above. In addition, cell free DNA from cells that have been destroyed and which can be retrieved from body fluids can be used for such screening. Such cell-free DNA is known to be disseminated from the brain into the blood-stream after head trauma. Specific techniques for identifying the tissue of origin of the cell-free DNA e.g. brain are now in existence and can be used to focus the analysis of circulating cell-free DNA methylation to particular organs of interest (Moss J, Magenheim J, Neiman D, et al). Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nature Communication 2018; 9: 1-19)

Cells and nucleic acids from any biological samples which contain nucleic acids can be used for the purpose of assessing or predicting TBI in a patient. Samples used for testing can be obtained from living or dead tissue and also archeological specimens containing cells or tissues. Nucleic acids include DNA or RNA, including for example, mRNA. Examples of biological samples that can be used for TBI screening include: any sample containing cell-free nucleic acids including cell-free DNA or RNA, skin, hair, follicles/roots, mucous membranes, internal body tissue. Examples of mucous membranes include cheek scrapings, buccal scrapings, or scrapings from the tongue for epigenomic analysis. For metabolomic analysis body fluids as well as tissue/cells can be used for analysis. Examples of body fluids include blood, saliva, urine, sweat, breath condensate and tears.

Biological samples can be obtained from patients (living or dead), including an adult, or a pediatric patient. The biological sample can be a body fluid, such as blood, urine or saliva. The biological sample can be tissue samples. As an example, cells and nucleic acids including DNA and RNA can be obtained from the biological sample for testing for TBI.

Embodiments include the use of genome-wide differences in cytosine methylation in DNA to screen for and determine risk or likelihood of TBI at any stage of life. These stages include the neonatal period (first 28 days after birth), infancy (up to 1 year of age), childhood (up to 10 years of age, adolescence (11 to 19 years of age), and adulthood (i.e. >19 years of age).

The results presented herein confirm that based on the differences in the level of methylation of the cytosine sites between TBI and normal cases throughout the whole human genome, the predisposition to or risk of having a TBI overall or subcategories of TBI can be determined.

Genome wide cytosine methylation study provides information on the orchestrated widespread activation and suppression of multiple genes and gene networks involved in brain injury after trauma. The approach does not require prior knowledge of the role of particular genes in brain injury in TBI. Further, hundreds of thousands of cytosine loci involving thousands of genes are evaluated simultaneously and in an unbiased fashion and can thus be used to accurately estimate the risk of TBI. Of further importance is the fact that cytosine loci outside of the genes can also control gene function. While we have mainly focused on CpG loci within and known/believed to be associated with particular genes it should be noted that methylation levels of loci situated outside of the gene can impact gene function and further contribute to the prediction of TBI.

In embodiments, the present disclosure confirms aberration or change in the methylation pattern of cytosine nucleotide occurs at multiple cytosine loci throughout the genome in individuals affected with different forms of TBI compared to individuals with normal brain development.

In embodiments, the present disclosure describes techniques and methods for predicting or estimating the risk of being diagnosed with TBI based on the differences in cytosine methylation at various DNA locations throughout the genome.

Currently no reliable clinically available biological method using cells, tissue or body fluids exist for predicting or estimating the risk of being diagnosed with TBI in individuals in the population.

TBI overall was evaluated and compared normal groups and cytosine nucleotides displaying statistically significant differences in methylation status throughout the genome were identified. Because of the extended coverage of cytosine nucleotides, some differentially methylated cytosines were located outside of CpG islands and outside of known genes. Nucleic acid cytosine methylation changes in either intragenic or extragenic cytosines individually (or in any combinations) can be used to detect or diagnose TBI.

The frequency or percentage of methylation of cytosine at one or more loci is compared with one or more controls. The controls include the frequency or percentage of methylation of cytosine of a well characterized population of normal (healthy) subjects or of a well characterized population of subjects known to have TBI. The TBI can be a specific form of TBI.

The present study reports a strong association between cytosine methylation status at a large number of cytosine sites throughout the genome using stringent False Discover Rate (FDR) analysis with q-values <0.05 and with many q-values as low as <1×10⁻³ depending on particular cytosine locus being considered (Table 2). A total of 18 cases of TBI and 18 normal controls underwent epigenomic analysis. Significant differences in cytosine methylation patterns at multiple loci throughout the DNA that was found in all TBI cases tested compared to normal. The particular cytosines disclosed in this application are located in or related with known genes. The findings are consistent with altered expression of multiple genes in TBI cases compared to controls.

The cytosine methylation markers reported enables targeted screening studies for the prediction and detection of TBI based on cytosine methylation throughout the genome. They also permit improved understanding of the mechanism of development of TBI for example by evaluating the cytosine methylation data using gene ontology analysis.

The cytosine evaluated in the present application includes but are not limited to cytosines in CpG islands located in the promoter regions of the genes. Other areas targeted and measured include the so called CpG island ‘shores’ located up to 2000 base pairs distant from CpG islands and ‘shelves’ which is the designation for DNA regions flanking shores. Even more distant areas from the CpG islands so called “seas” were analyzed for cytosine methylation differences. The extragenic cytosine loci, located outside of known genes (however they could potentially maintain long-distance control of unspecified genes) also detected TBI with moderate, good and excellent accuracy as indicated based on the AUROC. Thus, comprehensive and genome-wide analysis of cytosine methylation is performed.

Cell-Free DNA.

Cell free DNA (cfDNA) refers to DNA that has been released from cells as a result of natural cell death/turnover or as a result of disease processes. The cell free DNA is released into the circulation and rapidly broken down to DNA fragment. The techniques for harvesting of cell free DNA from the blood and other body fluids is well known in the arts (Li Y et al. Size separation of circulatory DNA in maternal plasma permits ready detection of fetal DNA polymorphisms. Clin Chem 2004; 50:1002-1011; Zimmerman B et al. Noninvasive prenatal aneuploidy testing of chromosomes 13, 18, 21, X, and Y, using targeted sequencing of polymorphic loci. Prenat Diagn 2012; 32:1233-41). In traumatic brain injury (TBI), damage to the blood brain barrier leads to increased circulating cfDNA in the blood and in severe TBI has been shown to correlate with prognosis (Macher H, Egea-Guerrero J J, Revuelto-Rey J et al. Role of early cell-free DNA levels decrease as a predictive marker of fatal outcome after severe traumatic brain injury. Clin Chim Acta 2012; 414:12-17). Epigenetic analysis of circulating cfDNA (from the brain after trauma) therefore represents a unique opportunity to evaluate brain function non-invasively (i.e. by analysis of the blood). Techniques for precision targeting of circulating cell free DNA from the brain will further enhance understanding of mechanism and the detection of TBI. Analyses of cell free DNA from different tissues e.g. the cortical neurons are now in existence (Moss J et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nature communication 2018; 9: 1-19). Targeting of brain cell free DNA and epigenomic analysis will further enhance understanding of mechanism and detection of TBI.

Statistical Analyses.

The present disclosure describes a method for estimating the individual risk of being diagnosed with TBI or even a particular type of TBI. This calculation can be based on logistic regression analysis leading to identification of the significant independent predictors among a number of possible predictors (e.g. methylation loci) known to be associated with increased risk of being diagnosed with TBI. Cytosine methylation levels at different loci can be used by themselves or in combination with other known risk predictors such as for example such as clinical test e.g. Standardized Assessment of Concussion (SAC) score. This is a clinical assessment test of functions that are likely to be affected by trauma including orientation, memory, neurological functions (e.g. sensation, concentration etc), delayed recall of words and ability to withstand exertion. In addition, clinical findings such as loss of consciousness (important but not a required diagnostic feature of concussion) can be combined with methylation changes for the prediction of concussion. The probability of an individual being affected with concussion can be determined based on the logistic equation:

P _(TBI)=1/1+e−(B1x ₁ +B2x ₂ +B3x ₃ . . . Bnx _(n))

where ‘x’ refers to the magnitude or quantity of the particular predictor (e.g. methylation level at a particular locus) and “β” or β-coefficient herein refers to the magnitude of change in the probability of the outcome (a particular type of TBI) for each unit change in the level of the particular predictor (x), the β values are derived from the results of the logistic regression analysis. These β-values would be derived from multivariable logistic regression analysis in a large population of affected and unaffected individuals. Values for x₁, x₂, x₃ etc., representing in this instance methylation percentage at different cytosine locus would be derived from the individual being tested while the n-values would be derived from the logistic regression analysis of the large reference population of affected (TBI) and unaffected cases mentioned above. Based on these values, an individual's probability of having a type of TBI can be quantitatively estimated. Probability thresholds are used to define individuals at high risk (e.g. a probability of ≤ 1/100 of TBI may be used to define a high risk individual triggering further evaluation, while individuals with low risk e.g. < 1/100 would require no further follow-up. The threshold used will among other factors be based on the diagnostic sensitivity (number of TBI cases correctly identified), specificity (number of non-TBI cases correctly identified as normal) risk and influenced by cost of CT or MRI scans and related interventions pursuant to the designation of an individual as “high risk” for TBI and such factors. Logistic regression analysis is well known as a method in disease screening for estimating an individual's risk for having a disorder. Computer programs such as ROCR package version 3.4 (https://CRAN.R-proiect.org/package=ROCR) can simplify and automate the process of generating the AUC values. In addition, other quantifiable parameters e.g. SAC score can be integrated into the logistic regression to calculate individual risk of Concussion.

Logistic regression analysis can also be used for calculation of sensitivity and specificity for the prediction of TBI based on methylation of cytosine loci.

It has been demonstrated that statistically highly significant differences exist in the percentage or level of methylation of individual cytosine nucleotides distributed throughout the genome both within and outside of the genes when cases with TBI are compared to normal unaffected cases. Cytosines demonstrating methylation differences are distributed both inside and outside of (CpG islands, shores) and genes. The disclosure describes methylation markers for distinguishing TBI from normal cases.

Particular embodiments describe a panel of cytosine markers for distinguishing individual categories of TBI from normal cases and also for distinguishing TBI as a group from normal cases without TBI. The disclosure includes risk assessment at any time or period during postnatal life.

Additional embodiments describe the use of statistical algorithms and methods for estimating the individual risk of being diagnosed with TBI based on methylation levels al informative cytosine loci.

Embodiments describe methods for diagnosing TBI based on measurement of the frequency or percentage methylation of cytosine nucleotides in various identified loci in the DNA of individuals. The present disclosure describes a method comprising the steps of: A) obtaining a sample from a patient; B) extracting DNA from blood specimens; C) assaying to determine the percentage methylation of cytosine al loci throughout the genome; D) comparing the cytosine methylation level of the patient to a well characterized population of normal and TBI groups; and E) calculating the individual risk of TBI based on the cytosine methylation level al different sites throughout the genome.

In embodiments, the sample is body fluid from which DNA is extracted for assessment of DNA methylation is blood. Examples of body fluid includes urine, and saliva. In embodiments, the sample is a tissue sample of a patient. Examples of tissue samples include hair and other sources of cells such as buccal swabs etc.

In embodiments, the methylation sites are used in many different combinations to calculate the probability of diagnosing TBI in an individual.

In embodiments, the patient is an adult. In embodiments, the patient is a newborn. In embodiments, the patient is a pediatric patient.

In embodiments, the disclosure describes determining the risk of being diagnosed with TBI at any time during any period of life from birth until death. Because concussion is a significant risk at the extremes of life, small children and the elderly and periods in between, young adolescents due to sports and recreational activities, adults in the military and adults from motor vehicular accidents and alcohol and drug consumption, the testing described for concussion screening can be performed at any age of life. In embodiments, the disclosure describes determining risk of being diagnosed with TBI as a pediatric patient. In embodiments, the disclosure describes determining risk of being diagnosed with mTBI as a pediatric patient. As pointed out however the testing can be justified at any age of life.

In embodiments, the DNA is obtained from cells. In embodiments, the DNA is cell free DNA.

In embodiments, the sample is obtained and stored for purposes of pathological examination. In embodiments, the sample is stored as slides, tissue blocks, or frozen. In embodiments, the TBI can be any of its types such as mTBI.

The present disclosure provides intragenic cytosine markers and their performance as represented by the Area under the ROC curve (AUC) and 95% Confidence Interval (CI) for the detection of TBI versus unaffected controls in Table 2.

In embodiments, measurement of the level or percentage methylation of cytosine nucleotides is obtained using gene or whole genome sequencing techniques. In embodiments, the assay is a bisulfite-based methylation assay or DNA methylation sequencing to identify methylation changes in individual cytosines throughout the genome.

In embodiments, the disclosure describes a method by which proteins transcribed from the genes described can be measured in body fluids and used to detect and diagnose mild TBI. Proteins are the products of gene transcriptional activity (‘gene expression’). Methylation changes in CpG classically results in altered expression of the relevant gene. It stands to reason therefore that the levels of mTBI related proteins in the blood for example will generally reflect the DNA methylation changes (induced by brain trauma) of the relevant genes.

In embodiments, proteins transcribed from related genes can be measured and quantitated in body fluids and or tissues of pregnant mothers or affected individuals.

In another embodiment mRNA produced by affected genes is measured in tissue or body fluids and mRNA levels can be quantitated to determine activity of said genes and used to estimate likelihood of accurately diagnosing TBI. In embodiments, the method further comprises the use of an mRNA genome-wide chip for the measurement of gene activity of genes genome-wide for screening any tissue or body fluids (including blood and saliva) containing mRNA.

Tables of Genes, Genomic Loci, and Metabolomic Markers.

Table 2 provides the list of CpG loci (and associated genes) that can be used individually or in combination for mTBI detection (18 mTBI and 18 unaffected controls). Table 3 provides concentration data from the metabolomic analysis of a subgroup of 17 cases and 18 controls used for combined epigenomic and metabolomic analyses for the prediction of concussion. Tables 4-8 show the performance of epigenetic, metabolomic and clinical and demographic markers individually and in combination for mTBI or concussion detection. Supplementary Tables S1-S3 show the performance of CpG loci in particular categories of genes (miRNA, ORF and LOC genes) for the prediction of mTBI. Supplemental Table S4 shows expanded epigenomic markers for pediatric concussion.

Table 2 provides an extended list of 412 genomic (CpG) loci. One or more, two or more, up to and including all 412 of the genomic loci in Table 2 can be selected for predicting TBI in a patient. This entire set of loci will be used to create a microarray for the for predicting TBI.

Likewise, one, one or more, two or more, up to and including all of the genomic loci and other predictors (metabolomic and clinical markers) in Tables 2-8 can be used in different combinations as laid out for the prediction of TBI in a patient. In embodiments, the one or more selected genomic loci have an AUC of ≥0.60, ≥0.65, ≥0.70, ≥0.75, ≥0.80, ≥0.85, ≥0.90, ≥0.95, ≥0.96, ≥0.97, ≥0.98, or ≥0.99. Ranges described throughout the application include the specified range, the sub-ranges within the specified range, the individual numbers within the range, and the endpoints of the range. For example, description of a range such as from one or more up to 412 includes subranges such as from one or more to 100 or more, from 10 or more to 20 or more, from one or more to five or more, as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, 10, 20, 100, and 173. Moreover, as further example, the description of a range of 0.75 would include all the individual numbers from 0.75 to 1.00 and including 0.75 and 1.0.

In embodiments, differentially methylated genes in the blood DNA of TBI patients include PTGDR, PTGER4, S1P1-S1P4, ADCY8, GRIN2D, PLCG2, FGD3, ARHGAP24, BDNF, PIK3CD, HSPA 1L, PTPrC, PTPN6, HLA-DMA, SIPA1, SIPAL2, INPP4A, FYN, PXN, CDC26, PDE4B, and WNT3. In embodiments, genes associated with TBI include LOC genes, such as LOC100134368 and LOC645323. In embodiments, genes associated with TBI include ORF genes such as C20rf40.

In embodiments, the genes associated with TBI include microRNAs such as miR-24-2, miR-548AS, miR-137, miR-365-1, miR-23A, and miR-27A. MicroRNA (miRNA) is an important epigenetic mechanism and exerts control over DNA methylation and suppresses gene expression among other functions. Therefore, the CpG methylation status of known microRNA genes can be measured instead of measuring actual miRNA levels in the blood to diagnose TBI. Given that DNA methylation status is known to correlate with gene expression, this approach can be used to identify miRNAs that are involved in brain injury.

TABLE 2 Differentially methylated 412 genes with Target ID, Gene ID, chromosome location, % methylation change (compared to controls), and FDR p-value for each gene identified in the analysis % Methylation % Methylation Fold TargetID CHR Gene ID Cases Control change FDR p-Val AUC CI_lower CI_upper cg06393885 5 GALNT10 6.40 17.50 0.37 9.13705E−22 0.92 0.82 1.00 cg20569893 17 FLOT2 7.50 19.10 0.39  9.8486E−22 0.90 0.80 1.00 cg00611535 12 RAB5B 5.63 11.66 0.48 4.16297E−09 0.90 0.80 1.00 cg16882751 1 FCMR 15.64 37.67 0.42 1.29837E−29 0.90 0.79 1.00 cg06233996 18 NEDD4L 4.52 19.01 0.24 1.62449E−31 0.89 0.78 1.00 cg24742298 17 SEZ6 6.36 24.31 0.26  1.3047E−30 0.89 0.78 1.00 cg19470964 15 LINC01585 7.68 16.03 0.48 6.30559E−13 0.89 0.77 1.00 cg24402518 10 PIK3AP1 8.67 24.02 0.36 3.95878E−31 0.89 0.77 1.00 cg13528854 4 BST1 6.18 14.64 0.42 1.43407E−14 0.87 0.75 0.99 cg05138188 19 KIRREL2 12.78 6.36 2.01 1.20458E−10 0.87 0.75 0.99 cg00832555 3 LRRC34 8.97 21.57 0.42 1.05316E−22 0.87 0.75 0.99 cg22407154 17 CCL4L1; CCL4L2 12.02 25.36 0.47 1.90004E−21 0.86 0.74 0.99 cg18786479 1 CD247 2.19 4.50 0.49 0.004573041 0.86 0.74 0.99 cg24129356 6 HLA-DMA 9.80 22.76 0.43 9.53863E−23 0.86 0.74 0.99 cg21115691 1 FCAMR 3.49 15.43 0.23 1.35261E−29 0.85 0.73 0.98 cg15762032 10 GAD2 5.44 14.50 0.38 4.59479E−17 0.85 0.73 0.98 cg07077221 3 HESRG; CACNA2D3 7.83 21.94 0.36  1.252E−28 0.85 0.73 0.98 cg03957481 22 KLHDC7B 1.91 9.56 0.20 7.81035E−18 0.85 0.73 0.98 cg24684739 11 ZFP91; LPXN; 3.54 7.56 0.47 6.93862E−06 0.85 0.72 0.98 ZFP91-CNTF cg13631572 14 ADCY4 3.64 11.95 0.30 2.77797E−17 0.85 0.72 0.98 cg20601481 11 ANKRD13D 3.20 10.10 0.32  1.4423E−13 0.85 0.72 0.98 cg12181083 8 SULF1 7.80 16.13 0.48 8.86432E−13 0.85 0.72 0.98 cg24104241 1 ACTA1 8.39 18.97 0.44 6.87272E−18 0.85 0.71 0.98 cg22026423 10 BUB3 6.28 17.50 0.36 2.73084E−22 0.85 0.71 0.98 cg19071452 2 NMI 6.57 18.15 0.36 6.33629E−23 0.85 0.71 0.98 cg07801620 11 CAT 13.91 28.13 0.49 5.50739E−22 0.84 0.71 0.97 cg01967102 3 KAT2B 14.51 33.55 0.43 2.43888E−30 0.84 0.71 0.97 cg03343063 1 PLEKHM2 5.69 19.34 0.29 7.31143E−31 0.84 0.71 0.97 cg03307717 21 U2AF1 10.75 22.02 0.49 2.15156E−17 0.84 0.71 0.97 cg17078116 8 NEFM 4.65 16.64 0.28 2.21457E−27 0.84 0.70 0.97 cg08218360 6 NUDT3; RPS10 10.38 22.03 0.47 1.08889E−18 0.84 0.70 0.97 cg16927040 3 SST 5.71 13.46 0.42 3.49497E−13 0.84 0.70 0.97 cg00698595 11 ETS1 7.22 15.26 0.47  1.4772E−12 0.83 0.70 0.97 cg20891481 3 FOXP1 6.50 14.28 0.46 1.57149E−12 0.83 0.70 0.97 cg17836354 22 MKL1 6.57 24.97 0.26 1.68483E−30 0.83 0.70 0.97 cg22836191 8 CHRNA6 5.51 17.89 0.31 4.63598E−27 0.83 0.69 0.97 ch.3.1226245F 3 ERC2 2.92 10.46 0.28 7.34476E−16 0.83 0.69 0.97 cg24954861 15 IGF1R 12.74 28.85 0.44 4.31675E−28 0.83 0.69 0.97 cg18490792 16 PLCG2 6.60 23.39 0.28 6.55642E−31 0.83 0.69 0.97 cg27599271 14 RIPK3 5.54 13.99 0.40 3.34965E−15 0.83 0.69 0.96 cg14503399 8 CPQ 6.25 21.75 0.29 3.01444E−31 0.82 0.69 0.96 cg19823803 4 KIT; KIT 7.43 18.64 0.40 9.86694E−21 0.82 0.69 0.96 cg00225623 13 SPATA13 5.50 14.33 0.38 2.40298E−16 0.82 0.69 0.96 cg03290752 3 TBL1XR1 5.70 13.43 0.42 3.76048E−13 0.82 0.69 0.96 cg01698105 6 LRRC16A 5.77 14.65 0.39 3.53967E−16 0.82 0.68 0.96 cg03530294 22 APOL3 4.99 14.22 0.35 3.59302E−18 0.82 0.68 0.96 cg12386061 3 CTDSPL 6.99 18.63 0.38 1.59598E−22 0.82 0.68 0.96 cg14666113 8 GATA4 3.63 12.60 0.29 2.43589E−19 0.82 0.68 0.96 cg15685268 11 IGSF9B 5.26 11.45 0.46 1.00771E−09 0.82 0.68 0.96 cg04385966 3 KLHL6 4.54 16.36 0.28 4.86807E−27 0.82 0.68 0.96 cg19946512 9 RNF38 7.83 19.34 0.40 4.76089E−21 0.82 0.68 0.96 cg26405376 5 RNF44 5.60 13.73 0.41 2.69917E−14 0.82 0.68 0.96 cg12205413 6 BACH2 6.75 14.81 0.46 4.98124E−13 0.82 0.68 0.96 cg25240468 2 CCT4 0.67 2.19 0.31 0.042088096 0.82 0.68 0.96 cg15665792 7 ELMO1 15.32 31.32 0.49  2.4182E−25 0.82 0.68 0.96 cg16709232 17 KIF19 3.21 6.81 0.47 3.62932E−05 0.82 0.68 0.96 cg05053923 5 LINC00992 4.61 11.69 0.39 1.65627E−12 0.82 0.68 0.96 cg24604213 2 SP140L 4.99 13.46 0.37 6.28074E−16 0.82 0.68 0.96 cg16049690 5 BTNL9 10.40 23.50 0.44 1.87114E−22 0.81 0.67 0.96 cg27384352 7 CDK5; SLC4A2 4.05 8.15 0.50 8.15584E−06 0.81 0.67 0.96 cg08067617 19 F2RL3 5.86 15.76 0.37 6.14802E−19 0.81 0.67 0.96 cg13565152 9 HINT2 6.51 3.14 2.07 5.30759E−05 0.81 0.67 0.96 cg16795830 6 LST1 4.28 18.04 0.24 1.02775E−31 0.81 0.67 0.96 cg07498879 22 OSM 4.74 20.61 0.23 3.79848E−31 0.81 0.67 0.96 cg26921036 4 TEC 7.78 24.27 0.32 5.47344E−31 0.81 0.67 0.96 cg25997979 10 ZMIZ1 2.86 12.27 0.23 3.69783E−22 0.81 0.67 0.96 cg15596749 1 BLACAT1 6.14 14.43 0.43 3.77171E−14 0.81 0.67 0.95 cg25769852 12 CD69 9.75 23.00 0.42 1.43041E−23 0.81 0.67 0.95 cg13620034 8 MSC 5.72 16.61 0.34 4.30693E−22 0.81 0.67 0.95 cg05601847 4 THAP9 9.55 26.16 0.37  5.8866E−31 0.81 0.67 0.95 cg05845319 3 EPM2AIP1; MLH1 5.35 11.08 0.48 1.34497E−08 0.81 0.66 0.95 cg17579384 1 GCSAML 7.14 15.58 0.46 1.39399E−13 0.81 0.66 0.95 cg04104119 5 LNPEP 10.46 28.28 0.37  1.2027E−30 0.81 0.66 0.95 cg23677243 15 MEIS2 8.37 18.44 0.45 1.44489E−16 0.81 0.66 0.95 cg25924217 17 NPTX1 5.47 13.71 0.40  9.9582E−15 0.81 0.66 0.95 cg04192740 5 STK10 5.97 18.98 0.31 3.05439E−28 0.81 0.66 0.95 cg00663077 4 ZFP42 10.05 21.77 0.46 3.33961E−19 0.81 0.66 0.95 cg04379606 16 ZNF75A; TIGD7 3.81 12.13 0.31 4.82615E−17 0.81 0.66 0.95 cg05851250 3 ANO10 7.15 22.84 0.31 3.38947E−31 0.81 0.66 0.95 cg21599974 4 ARHGAP24 5.68 13.21 0.43 1.24889E−12 0.81 0.66 0.95 cg09243716 8 DLC1 7.13 22.50 0.32 2.77884E−31 0.81 0.66 0.95 cg10303411 6 FAM65B 5.24 22.70 0.23 9.79114E−31 0.81 0.66 0.95 cg12378187 3 FEZF2 8.96 22.33 0.40 6.90954E−25 0.81 0.66 0.95 cg25563256 17 FGF11 12.37 25.83 0.48 1.99317E−21 0.81 0.66 0.95 cg24134845 10 HPSE2 3.03 7.86 0.39 4.31193E−08 0.81 0.66 0.95 cg26720543 2 INPP5D 4.61 19.78 0.23  2.4696E−31 0.81 0.66 0.95 cg00231338 12 PLBD1 11.22 26.86 0.42  1.6683E−28 0.81 0.66 0.95 cg27510024 2 RFX8 11.82 24.71 0.48 1.72808E−20 0.81 0.66 0.95 cg18351999 11 SIGIRR 6.28 13.50 0.46 2.09497E−11 0.81 0.66 0.95 cg18419977 11 SLC22A18AS; SLC22A18 6.13 15.17 0.40 3.47273E−16 0.81 0.66 0.95 cg16117781 1 TNFRSF9 16.06 32.85 0.49 9.26684E−27 0.81 0.66 0.95 cg17277890 11 TRIM22 6.06 15.00 0.40 5.52092E−16 0.81 0.66 0.95 cg07212778 2 C1QL2 2.06 9.06 0.23 2.03981E−15 0.80 0.66 0.95 cg20293777 4 HPGDS 6.28 20.07 0.31 3.35181E−30 0.80 0.66 0.95 cg03286855 7 RAPGEF5 12.31 29.45 0.42 8.04544E−31 0.80 0.66 0.95 cg05651417 7 SNX10 6.10 20.25 0.30 1.31649E−31 0.80 0.66 0.95 cg14415885 1 TBX15 2.62 9.01 0.29 9.05712E−13 0.80 0.66 0.95 cg18801599 17 TMEM101 4.70 10.90 0.43 3.60861E−10 0.80 0.66 0.95 cg25526061 10 ADAM8 4.80 19.29 0.25  1.6176E−31 0.80 0.65 0.95 cg02812947 8 ADCY8 4.31 12.72 0.34 1.24617E−16 0.80 0.65 0.95 cg00897144 13 ALOX5AP 7.93 17.86 0.44  1.1661E−16 0.80 0.65 0.95 cg13448968 2 CHPF 1.08 2.72 0.40 0.032177843 0.80 0.65 0.95 cg00514609 11 CRYAB; HSPB2 8.29 18.62 0.45 2.46986E−17 0.80 0.65 0.95 cg00157796 6 FNDC1 1.61 5.59 0.29 5.33361E−07 0.80 0.65 0.95 cg27035251 19 JAK3 6.88 14.78 0.47 1.59087E−12 0.80 0.65 0.95 cg22761241 1 LINC01225 11.65 27.59 0.42 7.66047E−29 0.80 0.65 0.95 cg06648277 10 NKX6-2 3.74 15.29 0.24 9.86624E−28 0.80 0.65 0.95 cg10464462 17 RFFL 3.03 15.46 0.20 4.44216E−32 0.80 0.65 0.95 cg15444626 17 SAMD14 5.95 12.28 0.48 1.42937E−09 0.80 0.65 0.95 cg04184297 17 SLC16A3 2.70 19.51 0.14 6.61457E−31 0.80 0.65 0.95 cg00803453 2 SPEG 4.35 11.65 0.37 2.40505E−13 0.80 0.65 0.95 cg27531206 1 UCK2 7.57 26.54 0.29 2.34644E−30 0.80 0.65 0.95 cg16478871 16 XPO6. 11.63 30.11 0.39  1.7731E−30 0.80 0.65 0.95 cg16536329 5 ZNF454 10.79 22.55 0.48 1.33747E−18 0.80 0.65 0.95 cg17240760 1 CD53 8.91 18.43 0.48 9.94145E−15 0.80 0.65 0.94 cg06470626 16 CDR2 8.54 17.59 0.49 7.20618E−14 0.80 0.65 0.94 cg03647767 10 JMJD1C 6.98 25.45 0.27 1.75723E−30 0.80 0.65 0.94 cg09965733 14 LRFN5 6.09 12.18 0.50 6.17764E−09 0.80 0.65 0.94 cg19651694 11 PHOX2A 5.40 14.97 0.36 1.27784E−18 0.80 0.65 0.94 cg23413809 2 SIX2 4.13 22.22 0.19 1.40809E−30 0.80 0.65 0.94 cg19391966 17 TNK1 3.90 13.72 0.28 1.20881E−21 0.80 0.65 0.94 cg00381233 7 VOPP1 7.99 23.05 0.35 2.64118E−31 0.80 0.65 0.94 cg06444282 1 VWA5B1 7.86 16.24 0.48 7.51673E−13 0.80 0.65 0.94 cg06675190 15 ACAN 3.07 10.25 0.30 1.44917E−14 0.79 0.64 0.94 cg20779757 15 ANPEP 7.14 16.13 0.44 5.42639E−15 0.79 0.64 0.94 cg04263436 6 B3GALT4 2.67 9.94 0.27 2.26722E−15 0.79 0.64 0.94 cg04510788 1 GRIK3 3.65 10.65 0.34 2.54762E−13 0.79 0.64 0.94 cg25643819 6 HLA-L 2.34 5.33 0.44 0.000272499 0.79 0.64 0.94 cg06460568 11 IGF2AS; INS- 3.91 10.69 0.37 1.97523E−12 0.79 0.64 0.94 IGF2; IGF2 cg14085715 7 MACC1 6.20 16.43 0.38 1.79412E−19 0.79 0.64 0.94 cg14373499 2 ACTR3 2.27 5.03 0.45 0.000740078 0.79 0.64 0.94 cg08923160 16 APOB48R 10.98 25.10 0.44 1.51052E−24 0.79 0.64 0.94 ch.10.1562909R 10 CCDC109A 5.12 10.75 0.48 1.64879E−08 0.79 0.64 0.94 cg00166343 17 CRLF3 13.49 27.64 0.49 3.47496E−22 0.79 0.64 0.94 cg21215767 2 EN1 8.21 17.05 0.48 1.17226E−13 0.79 0.64 0.94 cg05109049 17 EVI2B; NF1 9.38 26.66 0.35 8.78206E−31 0.79 0.64 0.94 cg25720793 1 F11R 4.11 12.80 0.32 9.75525E−18 0.79 0.64 0.94 cg23474501 10 GPR123 3.28 12.97 0.25 2.66714E−22 0.79 0.64 0.94 cg00983904 12 IFFO1 10.23 24.83 0.41 8.58091E−27 0.79 0.64 0.94 ch.8.1136903F 8 SNTG1 3.41 8.49 0.40 1.94795E−08 0.79 0.64 0.94 cg10143426 1 TP73; WDR8 4.24 13.34 0.32  7.8074E−19 0.79 0.64 0.94 cg16016319 1 TRIM63 6.48 19.98 0.32  6.157E−29 0.79 0.64 0.94 cg26693584 16 WWOX 7.18 25.13 0.29 1.30206E−30 0.79 0.64 0.94 cg23729050 2 KLF7 1.52 3.15 0.48 0.040426451 0.79 0.64 0.94 cg11753540 11 ADM 3.59 12.71 0.28 6.93513E−20 0.79 0.64 0.94 cg04052466 19 AMH 10.56 21.13 0.50 7.11424E−16 0.79 0.64 0.94 cg13785898 16 CKLF- 5.33 11.29 0.47 3.72082E−09 0.79 0.64 0.94 CMTM1; CKLF; TK2 cg01974370 1 NBPF20 5.30 15.55 0.34 9.20672E−21 0.79 0.64 0.94 cg13916762 6 NHSL1 8.17 16.77 0.49 4.03382E−13 0.79 0.64 0.94 cg13795063 17 RARA 3.96 10.21 0.39  6.1149E−11 0.79 0.64 0.94 cg11686792 13 RASA3 5.21 17.79 0.29 2.88503E−28 0.79 0.64 0.94 cg12881222 7 STEAP1B 9.85 22.39 0.44 1.47903E−21 0.79 0.64 0.94 cg02253612 7 STX1A 3.57 16.07 0.22 2.45427E−31 0.79 0.64 0.94 cg01307939 7 TMEM130 1.01 4.19 0.24 2.28342E−05 0.79 0.64 0.94 cg01636080 1 WASF2 10.58 23.94 0.44 6.13893E−23 0.79 0.64 0.94 cg19934381 17 B3GNTL1 6.51 14.75 0.44 1.13733E−13 0.78 0.63 0.94 cg20305095 2 CUL3 13.55 30.11 0.45 1.73681E−28 0.78 0.63 0.94 cg10109500 3 GHSR 7.68 17.43 0.44 1.89253E−16 0.78 0.63 0.94 cg18752527 2 HECW2 9.50 21.94 0.43 1.15181E−21 0.78 0.63 0.94 cg00091953 4 INPP4B 4.42 9.43 0.47 1.43101E−07 0.78 0.63 0.94 cg11572340 10 KCNIP2 5.67 12.03 0.47 8.15716E−10 0.78 0.63 0.94 cg15531403 8 NPBWR1 3.34 8.44 0.40  1.5969E−08 0.78 0.63 0.94 cg19793962 1 PIK3CD 10.19 21.29 0.48 1.71644E−17 0.78 0.63 0.94 cg27166718 6 PLN 11.58 27.41 0.42  1.2683E−28 0.78 0.63 0.94 cg14999168 11 PPFIA1 9.10 25.51 0.36  5.2131E−31 0.78 0.63 0.94 cg18954401 4 PRDM8 11.72 24.72 0.47 7.33058E−21 0.78 0.63 0.94 cg05694921 1 PTPRU 9.79 27.08 0.36 8.81648E−31 0.78 0.63 0.94 cg12691994 1 RASSF5 8.35 17.15 0.49 1.88136E−13 0.78 0.63 0.94 cg00249511 11 SCT 4.29 18.70 0.23 1.54523E−31 0.78 0.63 0.94 cg19588399 1 SYTL1 1.76 4.48 0.39 0.00056829 0.78 0.63 0.94 cg25552416 17 ZFP3 5.65 12.35 0.46  1.2184E−10 0.78 0.63 0.94 cg05433448 3 ATP2C1 4.93 11.83 0.42 9.58211E−12 0.78 0.63 0.93 cg23906261 10 GFRA1 6.12 16.17 0.38 4.40045E−19 0.78 0.63 0.93 cg21217024 11 GRIA4 4.09 15.45 0.26  2.3205E−26 0.78 0.63 0.93 cg15731655 12 HOTAIR 5.51 13.32 0.41 1.58554E−13 0.78 0.63 0.93 cg06203744 2 KIAA2012 12.08 27.57 0.44 3.98819E−27 0.78 0.63 0.93 cg17213699 12 LAG3 4.31 19.17 0.22 2.04164E−31 0.78 0.63 0.93 cg11041734 7 LIMK1 4.57 9.32 0.49 6.73342E−07 0.78 0.63 0.93 cg03887163 1 LMX1A 6.33 14.41 0.44 2.00385E−13 0.78 0.63 0.93 cg19677607 8 NEFM 2.66 10.76 0.25 4.54039E−18 0.78 0.63 0.93 eg14104842 7 NRCAM 12.99 27.28 0.48 5.62531E−23 0.78 0.63 0.93 cg16709110 11 SLC25A22 3.42 7.52 0.46 4.02408E−06 0.78 0.63 0.93 cg12610471 10 SPAG6 11.74 24.84 0.47 4.11033E−21 0.78 0.63 0.93 cg06147863 11 SPI1 11.94 25.19 0.47  2.7185E−21 0.78 0.63 0.93 cg00799539 12 ASCL1 8.16 19.23 0.42 1.81578E−19 0.78 0.62 0.93 cg27234340 17 ASGR1 5.63 13.35 0.42 3.53071E−13 0.78 0.62 0.93 cg13073773 14 GSC 3.91 15.26 0.26 1.17705E−26 0.78 0.62 0.93 cg16931416 10 INPP5A 4.00 13.77 0.29 2.40886E−21 0.78 0.62 0.93 cg08972954 5 ITK 5.77 18.05 0.32 2.54951E−26 0.78 0.62 0.93 cg26082690 10 KIAA1279 2.73 6.35 0.43 1.95375E−05 0.78 0.62 0.93 cg21340223 19 LAIR1 7.08 19.68 0.36 3.02029E−25 0.78 0.62 0.93 cg03521113 12 LRMP 2.88 12.95 0.22 2.36372E−24 0.78 0.62 0.93 cg26668608 2 PSD4 5.35 21.43 0.25 4.29128E−31 0.78 0.62 0.93 cg16902746 19 PTPRS 7.44 18.94 0.39 1.59183E−21 0.78 0.62 0.93 cg11696165 12 PXN 12.82 26.42 0.49 2.18201E−21 0.78 0.62 0.93 cg18024479 7 TFPI2 7.89 16.13 0.49 1.68159E−12 0.78 0.62 0.93 cg12798338 17 TMC8; TMC6 4.09 17.86 0.23 1.03499E−31 0.78 0.62 0.93 cg23724557 3 ZBTB20 9.65 20.97 0.46  1.5603E−18 0.78 0.62 0.93 cg13121699 2 ZNF804A 3.13 7.44 0.42 9.38151E−07 0.78 0.62 0.93 cg05070626 16 SYT17 3.65 10.52 0.35 6.01618E−13 0.78 0.62 0.93 cg11085070 9 TTF1 8.85 23.59 0.38 5.00196E−29 0.78 0.62 0.93 cg26182487 5 ABLIM3 3.84 11.55 0.33 3.89261E−15 0.77 0.62 0.93 cg04889100 17 ARSG 3.49 20.75 0.17 8.71941E−31 0.77 0.62 0.93 cg09870066 2 CNRIP1 6.28 15.03 0.42 3.02651E−15 0.77 0.62 0.93 cg25614907 5 CTD- 10.73 21.53 0.50 2.88755E−16 0.77 0.62 0.93 2194D22.4; IRX4 cg06086267 7 CUX1 5.84 21.94 0.27 4.32444E−31 0.77 0.62 0.93 cg26961808 7 FLNC 4.40 16.05 0.27 1.01563E−26 0.77 0.62 0.93 cg19485539 10 GFRA1 3.96 10.72 0.37 2.50302E−12 0.77 0.62 0.93 cg09092089 7 GIMAP7 7.30 24.42 0.30 7.94332E−31 0.77 0.62 0.93 cg27210998 17 GRB2 8.17 23.11 0.35 1.14086E−30 0.77 0.62 0.93 cg17736443 6 HIST1H2AJ 5.89 13.52 0.44 1.00813E−12 0.77 0.62 0.93 cg02694427 2 HOXD12 10.67 22.86 0.47 8.71401E−20 0.77 0.62 0.93 cg26902581 2 ITGA6 8.88 21.29 0.42 2.81476E−22 0.77 0.62 0.93 cg23807071 7 MAD1L1 6.73 17.61 0.38 1.10289E−20 0.77 0.62 0.93 cg07998137 3 MAGI1 8.87 20.81 0.43 5.05621E−21 0.77 0.62 0.93 cg18348337 5 MCC 4.86 11.30 0.43  1.2342E−10 0.77 0.62 0.93 cg07272395 4 MGAT4D 6.28 14.75 0.43  1.7309E−14 0.77 0.62 0.93 cg11257728 22 PARVG 6.20 12.97 0.48 2.10096E−10 0.77 0.62 0.93 cg01599770 1 PDE4B 10.20 22.27 0.46 5.81819E−20 0.77 0.62 0.93 cg03656996 15 PLCB2 3.97 15.69 0.25 8.93827E−28 0.77 0.62 0.93 cg05021267 20 POFUT1 12.33 28.61 0.43 5.05413E−29 0.77 0.62 0.93 cg20684253 16 PRKCB 5.70 13.96 0.41 1.54928E−14 0.77 0.62 0.93 cg27634876 14 PTGDR 3.43 12.55 0.27  3.6212E−20 0.77 0.62 0.93 cg02353184 10 SFMBT2 4.11 9.75 0.42 2.81805E−09 0.77 0.62 0.93 cg24211504 6 SIM1 7.71 17.94 0.43 1.11055E−17 0.77 0.62 0.93 cg13320626 5 ST8SIA4 4.41 9.00 0.49  1.2438E−06 0.77 0.62 0.93 cg09085842 6 HSPA1A; HSPA1L 1.47 5.98 0.25 1.53597E−08 0.77 0.62 0.93 cg15917517 12 APPL2 13.48 28.17 0.48 1.44843E−23 0.77 0.62 0.93 cg17680773 16 ATF7IP2 4.20 9.93 0.42 1.99543E−09 0.77 0.62 0.93 cg17669121 12 ATP6V0A2 15.43 31.30 0.49 6.70897E−25 0.77 0.62 0.93 cg01555705 16 CX3CL1 8.33 23.17 0.36 4.34559E−30 0.77 0.62 0.93 cg06085985 11 EFCAB4A 10.13 20.66 0.49 3.87875E−16 0.77 0.62 0.93 cg00037681 12 FAM113B 5.96 23.59 0.25 1.07984E−30 0.77 0.62 0.93 cg25975712 22 FAM19A5 2.57 8.60 0.30 9.34854E−12 0.77 0.62 0.93 cg12079322 2 GALNT13 2.17 10.63 0.20 3.84937E−20 0.77 0.62 0.93 cg03852144 5 GLRX 9.86 21.67 0.45 1.32248E−19 0.77 0.62 0.93 cg05040232 3 GP5 6.11 16.24 0.38 2.70631E−19 0.77 0.62 0.93 cg23849169 6 HCG4 7.23 16.76 0.43 2.40464E−16 0.77 0.62 0.93 cg18122874 22 IL17RA 6.01 21.26 0.28 2.59595E−31 0.77 0.62 0.93 cg04167833 5 LCP2 2.82 9.17 0.31 2.10401E−12 0.77 0.62 0.93 cg17608171 14 LINC01599 5.77 15.27 0.38 6.14739E−18 0.77 0.62 0.93 cg10821845 8 LY6H 3.70 12.00 0.31 3.84598E−17 0.77 0.62 0.93 cg27071460 5 PTGER4 2.63 14.56 0.18 1.55329E−31 0.77 0.62 0.93 cg02970551 1 RUNX3 5.40 11.41 0.47 3.29817E−09 0.77 0.62 0.93 cg05555207 12 TBX5 6.39 13.12 0.49 3.65195E−10 0.77 0.62 0.93 cg11852643 6 TCP11 2.61 16.52 0.16 1.13579E−31 0.77 0.62 0.93 cg07836142 6 ZSCAN23 4.82 12.72 0.38 1.69033E−14 0.77 0.62 0.93 cg03094728 7 AKAP9 11.40 24.54 0.46 1.63217E−21 0.77 0.61 0.92 cg19977280 22 APOBEC3D 4.86 14.80 0.33 1.78762E−20 0.77 0.61 0.92 cg23953831 1 CD101 6.80 22.82 0.30 4.13588E−31 0.77 0.61 0.92 cg26754426 3 CD200R1 9.07 25.03 0.36 3.99339E−31 0.77 0.61 0.92 cg10098353 2 CD28 8.64 24.90 0.35  4.771E−31 0.77 0.61 0.92 cg16732780 5 CDHR2 9.15 26.91 0.34 1.15971E−30 0.77 0.61 0.92 cg09142829 12 CLEC4A 13.52 27.38 0.49 2.00105E−21 0.77 0.61 0.92 cg13409544 14 ERO1A 4.91 12.70 0.39 4.43689E−14 0.77 0.61 0.92 cg27134386 4 GABRA2 4.42 9.95 0.44 9.34098E−09 0.77 0.61 0.92 cg00347757 13 GJB2 3.32 13.07 0.25 2.02329E−22 0.77 0.61 0.92 cg11083422 6 GLP1R 3.27 8.10 0.40  6.4454E−08 0.77 0.61 0.92 cg15508761 6 HIVEP2 8.15 16.96 0.48  1.2972E−13 0.77 0.61 0.92 cg07625529 2 HOXD10 10.64 22.81 0.47 9.24092E−20 0.77 0.61 0.92 cg12690996 1 KLHL21 10.76 25.69 0.42 4.52576E−27 0.77 0.61 0.92 cg03309253 19 LTBP4 4.27 14.05 0.30 6.35646E−21 0.77 0.61 0.92 cq06157602 5 PCDHGA1; PCDHGA2; 9.75 19.76 0.49 3.31189E−15 0.77 0.61 0.92 PCDHGA3; PCDHGB4; PCDHGB5; PCDHGA6; PCDHGA7; PCDHGA8; PCDHGA9; cg26070834 5 POLK 12.44 26.28 0.47 2.30562E−22 0.77 0.61 0.92 cg19113326 7 POU6F2 1.50 6.18 0.24 5.37024E−09 0.77 0.61 0.92 cg01782826 1 RCSD1 7.48 23.67 0.32 4.59035E−31 0.77 0.61 0.92 cg02996471 19 S1PR4 5.81 14.86 0.39 1.36125E−16 0.77 0.61 0.92 cg14287565 9 ARPC5L 2.96 6.39 0.46 6.22169E−05 0.77 0.61 0.92 cg09980176 19 CCDC151 7.25 20.34 0.36 1.81845E−26 0.77 0.61 0.92 cg00579036 6 CMAH 3.92 13.33 0.29 2.47604E−20 0.77 0.61 0.92 cg10926336 10 DCLRE1A 14.23 29.99 0.47  1.0936E−25 0.77 0.61 0.92 cg17848763 2 GYPC 4.33 15.39 0.28 6.33248E−25 0.77 0.61 0.92 cg01178680 15 ISLR2 7.73 26.26 0.29 1.82601E−30 0.77 0.61 0.92 cg19814116 1 KCNAB2 3.42 11.97 0.29 2.36749E−18 0.77 0.61 0.92 cg21455600 7 KCTD7 3.50 19.61 0.18  4.3641E−31 0.77 0.61 0.92 cg18025430 11 MS4A4A 8.95 26.21 0.34 8.70399E−31 0.77 0.61 0.92 cg12011642 17 MSI2 13.35 29.49 0.45 1.46855E−27 0.77 0.61 0.92 cg16046444 1 NR5A2 5.41 11.62 0.47  1.1876E−09 0.77 0.61 0.92 cg22060611 5 NRG2 4.97 10.56 0.47 1.57662E−08 0.77 0.61 0.92 cg21990556 11 OSBPL5 4.54 10.24 0.44 4.06009E−09 0.77 0.61 0.92 cg02147055 1 PTPRC 9.09 27.85 0.33 2.07274E−30 0.77 0.61 0.92 cg16514615 5 RASGEF1C 7.21 18.21 0.40 2.18228E−20 0.77 0.61 0.92 cg21720679 20 RBL1 14.39 31.49 0.46 3.96069E−29 0.77 0.61 0.92 cg17665121 4 TBC1D1 3.43 11.78 0.29 1.15573E−17 0.77 0.61 0.92 cg06786153 15 TBC1D2B 2.04 5.99 0.34 1.25749E−06 0.77 0.61 0.92 cg04869854 11 ATPGD1 10.56 23.44 0.45 1.06167E−21 0.76 0.60 0.92 cg20804072 17 CCL4L1 11.07 24.63 0.45  5.6851E−23 0.76 0.60 0.92 cg26433975 7 COM2 5.55 21.01 0.26 2.95218E−31 0.76 0.60 0.92 cg17929951 20 CD40 4.01 10.12 0.40 1.50857E−10 0.76 0.60 0.92 cg22499994 10 CUBN 8.15 20.21 0.40 3.44865E−22 0.76 0.60 0.92 cg19047707 2 ECEL1 3.29 13.62 0.24 1.76233E−24 0.76 0.60 0.92 cg22504849 5 FBXW11 6.78 16.53 0.41 2.05529E−17 0.76 0.60 0.92 cg16732077 6 FYN 7.62 15.43 0.49 9.96105E−12 0.76 0.60 0.92 cg12751305 1 KCNN3 5.48 12.93 0.42 1.28352E−12 0.76 0.60 0.92 cg00231422 16 NOL3 5.01 11.87 0.42 1.37135E−11 0.76 0.60 0.92 cg25206113 1 RORC. 7.29 23.22 0.31 3.91138E−31 0.76 0.60 0.92 cg23543318 4 SPON2 4.60 17.69 0.26 3.86033E−31 0.76 0.60 0.92 cg11824639 1 TCTEX1D1 4.85 15.01 0.32  3.5484E−21 0.76 0.60 0.92 cg22707438 11 DGKZ 8.75 21.63 0.40 9.09571E−24 0.76 0.60 0.92 cg04207218 21 ERG 6.79 16.78 0.40 4.73122E−18 0.76 0.60 0.92 cg08596591 9 FGD3 2.78 16.29 0.17 8.80088E−32 0.76 0.60 0.92 cg07223253 7 GIMAP8 4.16 15.12 0.27 6.29657E−25 0.76 0.60 0.92 cg01185682 17 HLF 1.75 4.34 0.40 0.000994576 0.76 0.60 0.92 cg05420790 12 HOXC10; HOXC-AS3 4.44 10.74 0.41 1.17153E−10 0.76 0.60 0.92 cg15930596 12 KCNA1 5.97 11.96 0.50 8.85939E−09 0.76 0.60 0.92 cg06345712 11 MAML2 11.01 24.27 0.45 3.23856E−22 0.76 0.60 0.92 cg15278646 15 OCA2 1.65 4.78 0.35 6.80447E−05 0.76 0.60 0.92 cg12823387 1 PTPN14 14.14 32.45 0.44 1.60559E−30 0.76 0.60 0.92 cg13431464 18 PTPN2 12.45 26.92 0.46 5.44044E−24 0.76 0.60 0.92 cg17051560 12 SOCS2 3.87 9.25 0.42 8.13509E−09 0.76 0.60 0.92 cg23340017 10 TLX1 7.57 20.60 0.37 8.53257E−26 0.76 0.60 0.92 cg23882019 8 TNFRSF10A 4.48 12.29 0.36 1.17991E−14 0.76 0.60 0.92 cg18058230 3 TNIK 7.83 20.49 0.38 2.28813E−24 0.76 0.60 0.92 cg19413693 1 VAV3 10.51 24.11 0.44 1.13911E−23 0.76 0.60 0.92 cg04136610 5 ADAMTS16 7.03 16.02 0.44 4.06145E−15 0.76 0.60 0.92 cg07703495 5 ADCY2 6.38 13.92 0.46 4.38412E−12 0.76 0.60 0.92 cg11905061 2 AGAP1 13.81 28.10 0.49 3.00947E−22 0.76 0.60 0.92 cg07117012 12 AQP2 10.88 24.78 0.44 4.40563E−24 0.76 0.60 0.92 cg08493294 2 DNMT3A 8.63 19.41 0.44 3.65131E−18 0.76 0.60 0.92 cg24644188 5 FYB 8.86 21.72 0.41 1.41616E−23 0.76 0.60 0.92 cg14307443 4 GALNTL6 2.47 9.70 0.25 1.44757E−15 0.76 0.60 0.92 cg06951565 5 GRM6 9.16 24.42 0.37 3.85776E−30 0.76 0.60 0.92 cg11284811 1 HENMT1 2.57 5.65 0.45 0.000219182 0.76 0.60 0.92 cg18172877 5 IRX4 9.51 20.24 0.47 3.70197E−17 0.76 0.60 0.92 cg21448352 2 KLHL29 11.22 26.80 0.42 2.47599E−28 0.76 0.60 0.92 cg12374834 1 LAPTM5 7.40 24.42 0.30 7.54815E−31 0.76 0.60 0.92 cg26778001 19 LILRB1 6.03 13.96 0.43 2.44007E−13 0.76 0.60 0.92 cg17527798 3 LTF 9.71 20.68 0.47 1.33291E−17 0.76 0.60 0.92 cg05364588 3 MECOM 7.86 19.20 0.41 1.45702E−20 0.76 0.60 0.92 cg06499262 16 NFAT5 11.06 23.09 0.48 5.16126E−19 0.76 0.60 0.92 cg06252985 11 PC 3.61 8.92 0.40 7.59572E−09 0.76 0.60 0.92 cg13549444 12 PTPN6 4.24 18.78 0.23 1.68008E−31 0.76 0.60 0.92 cg03050022 15 RAB27A 11.92 25.74 0.46 8.58867E−23 0.76 0.60 0.92 cg25747192 3 RASSF1 6.44 16.57 0.39 6.72405E−19 0.76 0.60 0.92 cg05767930 12 RHOF 3.34 17.03 0.20 9.87378E−32 0.76 0.60 0.92 cg13329322 2 SGPP2 12.56 30.41 0.41 1.22587E−30 0.76 0.60 0.92 cg23141934 19 SWSAP1 8.05 21.61 0.37 1.07227E−26 0.76 0.60 0.92 cg21201099 17 TBX2 8.55 19.82 0.43 1.47177E−19 0.76 0.60 0.92 cg15615160 17 TEKT1 1.98 4.39 0.45 0.002541547 0.76 0.60 0.92 cg27641628 7 TMEM140 3.38 9.01 0.37 7.21132E−10 0.76 0.60 0.92 cg09474331 19 TTYH1 2.77 12.76 0.22 2.59257E−24 0.76 0.60 0.92 cg08112940 6 ADGB 3.71 14.35 0.26  1.0642E−24 0.75 0.59 0.91 cg16909402 1 CD48 11.07 24.05 0.46 1.96713E−21 0.75 0.59 0.91 cg00339488 4 DCHS2 5.42 15.30 0.35 1.66422E−19 0.75 0.59 0.91 cg23338503 5 DOCK2 7.26 14.88 0.49 1.47167E−11 0.75 0.59 0.91 cg20132609 13 DOCK9 3.48 13.63 0.26 1.67637E−23 0.75 0.59 0.91 cg08288130 8 DOK2 10.76 26.21 0.41 1.50612E−28 0.75 0.59 0.91 cg10977770 1 ESRRG 12.17 25.21 0.48 1.44257E−20 0.75 0.59 0.91 cg16441238 14 EVL 9.39 21.02 0.45 1.30735E−19 0.75 0.59 0.91 cg07106633 8 FABP5 3.17 6.80 0.47 3.04945E−05 0.75 0.59 0.91 cg14065576 1 GSTM2 2.45 6.50 0.38 1.39644E−06 0.75 0.59 0.91 cg22715094 6 HSPA1A 5.31 10.69 0.50 7.73312E−08 0.75 0.59 0.91 cg03822198 12 MAP1LC3B2 3.26 14.42 0.23 2.22382E−27 0.75 0.59 0.91 cg14201424 4 MGC45800 2.92 15.81 0.18 5.93887E−32 0.75 0.59 0.91 cg07637516 10 MXI1 7.48 15.65 0.48 1.16087E−12 0.75 0.59 0.91 cg18150439 2 NXPH2 2.18 11.68 0.19 8.33465E−24 0.75 0.59 0.91 cg26256223 8 OPLAH 4.17 15.97 0.26 1.06488E−27 0.75 0.59 0.91 cg06604289 1 PRDM2 7.47 16.11 0.46  8.5933E−14 0.75 0.59 0.91 cg21899942 14 RGS6 6.37 13.74 0.46  1.1226E−11 0.75 0.59 0.91 cg09762242 11 SIPA1 7.31 24.02 0.30 6.27891E−31 0.75 0.59 0.91 cg14015044 8 TNFRSF10C 0.92 10.12 0.09 1.18898E−25 0.75 0.59 0.91 cg26413174 1 TTC22 3.58 10.27 0.35  1.5995E−12 0.75 0.59 0.91 cg26470340 10 ARHGAP21 3.87 1.87 2.07 0.010553676 0.75 0.59 0.91 cg14025053 8 BAALC 6.02 15.66 0.38 5.28266E−18 0.75 0.59 0.91 cg03167496 11 BDNF 6.20 13.42 0.46 2.00111E−11 0.75 0.59 0.91 cg24674189 13 BRCA2 11.86 26.00 0.46 1.05213E−23 0.75 0.59 0.91 cg06159486 10 CACNB2 2.36 6.06 0.39 8.16641E−06 0.75 0.59 0.91 cg01729827 20 CBLN4 7.16 16.46 0.44 9.04134E−16 0.75 0.59 0.91 cg18507129 3 CHST2 3.35 6.97 0.48 3.90766E−05 0.75 0.59 0.91 cg04207385 6 CNR1 4.23 10.75 0.39 2.07021E−11 0.75 0.59 0.91 cg22019177 11 DIXDC1 4.78 14.94 0.32 2.83692E−21 0.75 0.59 0.91 cg03606646 6 GFOD1 9.24 18.88 0.49 9.55987E−15 0.75 0.59 0.91 cg21726846 3 KALRN 5.63 16.14 0.35  4.6619E−21 0.75 0.59 0.91 cg21696827 17 KIAA0753 12.78 27.51 0.46 2.47093E−24 0.75 0.59 0.91 cg26663686 8 LY6H 4.23 8.50 0.50  4.5937E−06 0.75 0.59 0.91 cg07200038 16 MMP25 9.66 25.60 0.38 3.94704E−31 0.75 0.59 0.91 cg01151966 10 NRG3 4.74 11.87 0.40 1.64223E−12 0.75 0.59 0.91 cg10235355 17 PITPNM3 10.07 22.08 0.46 6.57834E−20 0.75 0.59 0.91 cg01364478 1 PRRX1 8.97 18.25 0.49 4.31121E−14 0.75 0.59 0.91 cg19239041 20 RASSF2 7.22 20.38 0.35  1.0008E−26 0.75 0.59 0.91 cg15480817 5 TMEM171 3.67 13.63 0.27 1.70455E−22 0.75 0.59 0.91 cg14426999 20 TOX2 2.61 6.46 0.40 5.11798E−06 0.75 0.59 0.91 cg24107270 7 WDR86 9.85 24.07 0.41 3.49563E−26 0.75 0.59 0.91 ch.15.814613R 15 MYO1E 3.01 6.59 0.46 3.25211E−05 0.75 0.59 0.91 cg13655635 9 NR4A3 7.33 23.11 0.32 3.57028E−31 0.75 0.59 0.91 cg07529534 8 ANK1 1.70 3.40 0.50 0.034049193 0.75 0.58 0.91 cg08024887 11 CCDC88B 4.34 18.33 0.24 1.19111E−31 0.75 0.58 0.91 cg16862641 6 COL9A1 2.44 5.07 0.48 0.001453983 0.75 0.58 0.91 cg27003782 10 CPXM2 4.61 11.14 0.41 4.41099E−11 0.75 0.58 0.91 cg16373526 11 CTTN 11.92 28.48 0.42 2.58821E−30 0.75 0.58 0.91 cg10788355 13 ELF1 6.84 14.62 0.47 2.93979E−12 0.75 0.58 0.91 cg22796507 1 FOXE3 3.93 10.70 0.37 2.26228E−12 0.75 0.58 0.91 cg08159065 5 GABRB2 9.05 18.68 0.48 7.60104E−15 0.75 0.58 0.91 cg25781595 19 GRIN2D 7.87 16.93 0.46 1.76271E−14 0.75 0.58 0.91 cg02326386 19 MOBKL2A 6.86 22.46 0.31 3.20421E−31 0.75 0.58 0.91 cg04961466 4 NPY5R 5.62 15.55 0.36 2.33102E−19 0.75 0.58 0.91 cg17480467 10 PAX2 4.16 10.60 0.39 2.77998E−11 0.75 0.58 0.91 cg17336584 6 PHACTR1 4.47 10.28 0.44 2.12568E−09 0.75 0.58 0.91 cg26723002 8 PLAG1; CHCHD7 6.41 15.26 0.42 2.24233E−15 0.75 0.58 0.91 cg15978357 4 RHOH 3.07 14.33 0.21 4.12129E−28 0.75 0.58 0.91 cg04228935 21 RUNX1 3.95 13.03 0.30  3.5543E−19 0.75 0.58 0.91 cg03055966 11 SBF2 6.11 14.76 0.41 3.77574E−15 0.75 0.58 0.91 cg13221285 2 SH3RF3 12.95 29.35 0.44 1.11974E−28 0.75 0.58 0.91 cg12795959 20 SIRPB1 4.79 14.79 0.32 8.73007E−21 0.75 0.58 0.91 cg02737619 1 SKI 3.40 10.92 0.31 4.04536E−15 0.75 0.58 0.91 cg20956278 22 SLC16A8 4.68 10.76 0.44 6.81788E−10 0.75 0.58 0.91 cg23192776 21 TIAM1 3.27 15.62 0.21 1.64984E−31 0.75 0.58 0.91 cg26309194 21 TMPRSS2 8.37 17.15 0.49 2.30126E−13 0.75 0.58 0.91 cg09158314 13 TNFSF13B 4.91 11.23 0.44 2.66146E−10 0.75 0.58 0.91 cg23724711 12 WIBG 5.94 17.03 0.35 2.09182E−22 0.75 0.58 0.91 cg02017282 17 WNT3 6.25 14.00 0.45 1.10829E−12 0.75 0.58 0.91 cg00622863 9 PRPF4; CDC26 1.66 4.73 0.35  8.7017E−05 0.75 0.58 0.91

SUPPLEMENTARY TABLE S1 Differentially Methylated microRNAs in mTBI. % % microRNA FDR Fold Methylation Methylation TargetID CHR ID p-Val chance Cases Control AUC CI_lower CI_upper cg08528170 19 MIR24-2 7.36464E−14 0.412 5.568 13.530 0.843 0.711 0.975 cg11432250 13 MIR548AS 9.38502E−17 0.484 10.114 20.893 0.824 0.685 0.963 cg09918657 16 MIR193B 8.42165E−16 0.399 5.834 14.610 0.809 0.664 0.953 cg04293733 1 MIR137 1.02473E−07 0.350 2.427 6.925 0.802 0.656 0.949 cg22388260 16 MIR365-1 3.81667E−31 0.273 5.969 21.855 0.787 0.636 0.938 cg08528170 19 MIR23A 7.36464E−14 0.412 5.568 13.530 0.843 0.711 0.975 cg08528170 19 MIR27A 7.36464E−14 0.412 5.568 13.530 0.843 0.711 0.975

SUPPLEMENTARY TABLE S2 Differentially Methylated 12 ORF genes. % % FDR Fold Methylation Methylation TargetID CHR ORF p-Val chance Cases Control AUC CI_lower CI_upper cg11252953 17 C17orf101 4.75647E−31 0.320 7.660 23.911 0.747 0.585 0.909 cg03081691 1 C1orf55 8.99759E−32 0.271 5.036 18.579 0.864 0.741 0.987 cg27166718 6 C6orf204  1.2683E−28 0.422 11.581 27.410 0.784 0.632 0.936 cg11337945 5 C5orf38 3.85929E−09 0.436 4.378 10.047 0.778 0.624 0.931 cg10883375 14 C14orf43 1.58015E−30 0.311 6.265 20.145 0.775 0.620 0.929 cg10050900 1 C1orf200  1.6683E−14 0.402 5.486 13.653 0.765 0.608 0.923 cg03846926 10 C10orf140 1.48757E−05 0.439 2.910 6.623 0.759 0.600 0.918 cg14826711 1 C1orf114  2.3089E−16 0.366 4.958 13.563 0.756 0.597 0.916 cg21542248 14 C14orf23 4.07572E−09 0.331 2.503 7.561 0.756 0.597 0.916 cg06499647 2 C2orf40 9.50633E−06 0.472 3.536 7.489 0.750 0.589 0.911 cg12860686 5 C5orf38 2.48351E−18 0.335 4.590 13.694 0.775 0.620 0.929 cg14025053 8 C8orf56 5.28266E−18 0.385 6.025 15.664 0.750 0.589 0.911

SUPPLEMENTARY TABLE S3 Differentially Methylated 18 LOC genes. % % LOC FDR Fold Methylation Methylation TargetID CHR Genes p-Val chance Cases Control AUC CI_lower CI_upper cg24956533 15 LOC145845 3.14447E−29 0.475 16.051 33.762 0.877 0.759 0.994 cg14536682 3 LOC339862 4.77947E−30 0.457 14.799 32.383 0.753 0.593 0.913 cg23307878 10 LOC105376360 1.31813E−30 0.450 14.710 32.683 0.765 0.608 0.923 cg12608247 2 LOC643387 2.14417E−14 0.478 8.447 17.678 0.765 0.608 0.923 cg12608247 2 LOC151174 2.14417E−14 0.478 8.447 17.678 0.765 0.608 0.923 cg24662666 10 LOC282997 3.46333E−31 0.325 7.573 23.298 0.787 0.636 0.938 cg24603047 2 LOC101927156 8.26506E−31 0.279 6.652 23.830 0.818 0.677 0.959 cg05908371 20 LOC284798 4.12897E−14 0.439 6.617 15.071 0.784 0.632 0.936 cg07044757 15 LOC145845 3.68152E−17 0.401 6.325 15.794 0.855 0.728 0.982 cg00037681 12 LOC100233209 1.07984E−30 0.253 5.962 23.595 0.772 0.616 0.927 cg20585676 2 LOC100132215 1.29385E−25 0.326 5.862 17.966 0.756 0.597 0.916 cg12309653 15 LOC145845 2.25346E−31 0.280 5.848 20.868 0.784 0.632 0.936 cg26668608 2 LOC440839 4.29128E−31 0.250 5.355 21.435 0.778 0.624 0.931 cg11860632 1 LOC100506022 5.40048E−31 0.224 4.754 21.216 0.753 0.593 0.913 cg00764796 16 LOC100134368 1.92945E−15 0.362 4.625 12.771 0.762 0.604 0.920 cg23543318 4 LOC100130872 3.86033E−31 0.260 4.602 17.691 0.762 0.604 0.920 cg11724135 20 LOC284798 2.36872E−19 0.314 4.239 13.508 0.765 0.608 0.923 cg12393318 5 LOC645323  3.8141E−22 0.275 3.750 13.644 0.806 0.660 0.951

TABLE 3 Metabolomics Results. q-value Fold Name Mean (SD) of 0 Mean (SD) of 1 p-value (FDR) Change 0/1 p.value.origin C0 35.280 (8.181) 37.300 (10.120) 0.8901 0.9129 −1.01 Down 0.89015 C10 0.171 (0.096) 0.195 (0.084) 0.2862 (W) 0.7245 −1.1 Down 0.28615 C101 0.365 (0.091) 0.341 (0.041) 0.1335 0.7614 1.11 Up 0.13352 C102 0.034 (0.011) 0.034 (0.007) 0.9563 (W) 0.9129 1.04 Up 0.95633 C12 0.083 (0.036) 0.098 (0.042) 0.2705 0.7245 −1.15 Down 0.27049 C12—DC 0.049 (0.004) 0.048 (0.003) 0.3384 0.8593 1.06 Up 0.33839 C121 0.293 (0.064) 0.299 (0.039) 0.8011 (W) 0.9248 1.02 Up 0.80109 C14 0.033 (0.013) 0.036 (0.015) 0.7014 (W) 0.9129 −1.09 Down 0.70135 C141 0.085 (0.024) 0.093 (0.033) 0.5326 0.9129 −1.06 Down 0.53262 C141—OH 0.011 (0.004) 0.012 (0.005) 0.5082 0.9129 −1.08 Down 0.50821 C142 0.034 (0.025) 0.035 (0.022) 0.6532 (W) 0.9129 −1.02 Down 0.6532 C142—OH 0.006 (0.003) 0.006 (0.002) 0.8351 (W) 0.9129 −1.05 Down 0.83514 C16 0.087 (0.025) 0.099 (0.034) 0.5612 (W) 0.9129 −1.09 Down 0.56116 C16—OH 0.005 (0.001) 0.005 (0.003) 0.6637 0.9746 −1.07 Down 0.66367 C161 0.018 (0.010) 0.021 (0.012) 0.6532 (W) 0.9129 −1.14 Down 0.6532 C161—OH 0.009 (0.003) 0.011 (0.006) 0.6532 (W) 0.9129 −1.19 Down 0.6532 C162 0.009 (0.004) 0.010 (0.004) 0.9563 (W) 0.9511 1 Up 0.95633 C162—OH 0.013 (0.002) 0.013 (0.002) 0.4103 (W) 0.9746 1.03 Up 0.41032 C18 0.033 (0.010) 0.041 (0.030) 0.7341 (W) 0.9746 −1.22 Down 0.73413 C181 0.079 (0.030) 0.086 (0.036) 0.7341 (W) 0.9189 −1.04 Down 0.73413 C181—OH 0.006 (0.003) 0.007 (0.007) 0.5612 (W) 0.9453 −1.25 Down 0.56116 C182 0.045 (0.024) 0.040 (0.015) 0.7507 (W) 0.9262 1.18 Up 0.75071 C2 5.758 (3.916) 6.891 (3.873) 0.2666 (W) 0.7245 −1.18 Down 0.26658 C3 0.453 (0.154) 0.409 (0.163) 0.2453 0.9129 1.17 Up 0.24527 C3—OH 0.007 (0.002) 0.007 (0.003) 0.7461 0.9074 −1.04 Down 0.74606 C31 0.010 (0.004) 0.009 (0.003) 0.1547 (W) 0.9129 1.17 Up 0.15473 C4 0.222 (0.161) 0.157 (0.052) 0.2302 (W) 0.9129 1.46 Up 0.2302 C3—DC C4—OH 0.044 (0.040) 0.052 (0.055) 0.9563 (W) 0.9746 −1.17 Down 0.95633 C41 0.014 (0.005) 0.013 (0.003) 0.1247 (W) 0.7614 1.11 Up 0.12474 C5 0.149 (0.047) 0.147 (0.078) 0.2666 (W) 0.9129 1.1 Up 0.26658 C5—M—DC 0.014 (0.005) 0.016 (0.003) 0.4489 (W) 0.7614 −1.07 Down 0.44894 C5—OH C3—DC—M 0.029 (0.009) 0.027 (0.006) 0.3979 (W) 0.9129 1.15 Up 0.39789 C51 0.015 (0.004) 0.015 (0.007) 0.8695 (W) 0.9987 1.08 Up 0.86951 C51—DC 0.008 (0.004) 0.009 (0.004) 0.8579 0.9189 −1.02 Down 0.85793 C6 C41—DC 0.048 (0.019) 0.050 (0.013) 0.7448 0.9129 −1.04 Down 0.74479 C5—DC C6—OH 0.014 (0.006) 0.014 (0.005) 0.7014 (W) 0.9831 1.03 Up 0.70135 C61 0.012 (0.003) 0.011 (0.003) 0.3172 (W) 0.9982 1.06 Up 0.31725 C7—DC 0.023 (0.013) 0.031 (0.015) 0.0641 (W) 0.7245 −1.33 Down 0.06408 C8 0.113 (0.045) 0.126 (0.038) 0.3619 (W) 0.7245 −1.06 Down 0.36195 C9 0.017 (0.005) 0.017 (0.006) 0.7144 0.9829 1.04 Up 0.71444 lysoPC a C140 2.798 (0.555) 2.871 (0.961) 0.3737 (W) 0.9189 1.02 Up 0.3737 lysoPC a C160 74.220 (27.205) 76.882 (54.845) 0.2571 (W) 0.9129 1 Up 0.25714 lysoPC a C161 1.842 (0.804) 2.099 (1.918) 0.5034 (W) 0.9792 −1.08 Down 0.50343 lysoPC a C170 1.298 (0.574) 1.550 (1.076) 0.8181 (W) 0.9511 −1.15 Down 0.81808 lysoPC a C180 23.206 (8.058) 22.434 (14.789) 0.1247 (W) 0.7245 1.07 Up 0.12474 lysoPC a C181 13.246 (5.032) 13.999 (8.181) 0.6532 (W) 0.9746 −1.02 Down 0.6532 lysoPC a C182 31.394 (15.117) 25.829 (9.775) 0.1213 0.7245 1.26 Up 0.12132 lysoPC a C203 1.630 (0.795) 1.681 (1.146) 0.7177 (W) 0.9982 1.03 Up 0.71768 lysoPC a C204 4.642 (1.905) 4.579 (1.792) 0.6374 (W) 0.9831 1.05 Up 0.63744 lysoPC a C240 0.109 (0.024) 0.107 (0.020) 0.8011 (W) 0.9746 1.07 Up 0.80109 lysoPC a C260 0.133 (0.040) 0.128 (0.033) 0.7014 (W) 0.9453 1.12 Up 0.70135 lysoPC a C261 0.057 (0.014) 0.058 (0.013) 0.7073 0.9746 1.03 Up 0.70729 lysoPC a C280 0.142 (0.035) 0.132 (0.023) 0.3857 (W) 0.9129 1.15 Up 0.38568 lysoPC a C281 0.146 (0.045) 0.148 (0.029) 0.6532 (W) 0.9129 1.05 Up 0.6532 PC aa C240 0.091 (0.026) 0.078 (0.019) 0.0994 (W) 0.7245 1.22 Up 0.0994 PC aa C260 0.645 (0.076) 0.606 (0.061) 0.045  0.7245 1.11 Up 0.05505 PC aa C281 1.605 (0.527) 1.554 (0.490) 0.3391 (W) 0.9129 1.09 Up 0.33914 PC aa C300 2.328 (0.902) 2.281 (0.667) 0.4203 0.9453 1.08 Up 0.42028 PC aa C320 11.359 (2.299) 12.204 (2.842) 0.5553 0.8301 −1.04 Down 0.5553 PC aa C321 7.265 (5.627) 6.839 (3.743) 0.9738 (W) 0.9511 1.13 Up 0.97379 PC aa C322 2.464 (1.414) 2.092 (0.778) 0.0427 0.8116 1.26 Up 0.09271 PC aa C323 0.264 (0.065) 0.262 (0.063) 0.7177 (W) 0.9829 1.05 Up 0.71768 PC aa C341 142.817 (46.079) 152.600 (48.488) 0.7867 0.9129 −1.03 Down 0.78673 PC aa C342 385.200 (67.808) 354.294 (51.684) 0.0484 0.7245 1.12 Up 0.05837 PC aa C343 6.376 (2.404) 5.949 (1.490) 0.2032 0.9129 1.13 Up 0.20323 PC aa C344 0.725 (0.364) 0.669 (0.196) 0.2557 0.9189 1.14 Up 0.25568 PC aa C360 2.943 (0.883) 2.840 (0.771) 0.433  0.9129 1.09 Up 0.43296 PC aa C361 29.867 (9.379) 29.682 (12.012) 0.5176 (W) 0.9189 1.05 Up 0.51757 PC aa C362 233.697 (64.646) 198.588 (46.993) 0.0267 0.7245 1.22 Up 0.02665 PC aa C363 91.330 (28.542) 87.335 (23.025) 0.3495 0.9829 1.09 Up 0.34947 PC aa C364 143.720 (38.415) 140.759 (34.059) 0.5596 0.9745 1.05 Up 0.5596 PC aa C365 4.723 (2.940) 4.967 (2.387) 0.7014 (W) 0.9189 −1.01 Down 0.70135 PC aa C366 0.210 (0.115) 0.200 (0.076) 0.7842 (W) 1 1.12 Up 0.78419 PC aa C380 1.972 (0.642) 2.074 (0.619) 0.4489 (W) 0.9129 −1 Down 0.44894 PC aa C383 38.973 (14.699) 37.900 (12.591) 0.8523 (W) 0.9746 1.09 Up 0.85229 PC aa C384 94.383 (27.573) 94.335 (27.926) 0.7379 0.9562 1.03 Up 0.73789 PC aa C385 24.430 (7.747) 27.212 (7.970) 0.4939 0.7245 −1.07 Down 0.4939 PC aa C386 42.193 (12.534) 43.606 (15.396) 0.9409 0.9829 1.01 Up 0.94091 PC aa C401 0.282 (0.064) 0.268 (0.048) 0.6218 (W) 0.9129 1.1 Up 0.62185 PC aa C402 0.243 (0.074) 0.243 (0.082) 0.7341 (W) 0.9189 1.04 Up 0.73413 PC aa C403 0.357 (0.085) 0.381 (0.083) 0.8028 0.9129 −1.02 Down 0.80279 PC aa C404 4.444 (1.614) 4.581 (1.756) 0.9563 (W) 0.9189 1.02 Up 0.95633 PC aa C405 6.391 (2.411) 6.812 (2.560) 0.5761 (W) 0.9129 −1.02 Down 0.57607 PC aa C406 17.207 (6.186) 16.210 (6.170) 0.5464 (W) 0.9129 1.11 Up 0.54644 PC aa C420 0.486 (0.155) 0.525 (0.121) 0.2302 (W) 0.7245 −1.04 Down 0.2302 PC aa C421 0.232 (0.079) 0.252 (0.058) 0.2390 (W) 0.7245 −1.05 Down 0.23895 PC aa C422 0.165 (0.046) 0.172 (0.034) 0.6691 (W) 0.9189 1.01 Up 0.6691 PC aa C424 0.180 (0.036) 0.201 (0.051) 0.4213 0.7245 −1.07 Down 0.42132 PC aa C425 0.202 (0.056) 0.226 (0.066) 0.2479 (W) 0.7245 −1.07 Down 0.24793 PC aa C426 0.294 (0.074) 0.340 (0.087) 0.274  0.7245 −1.11 Down 0.27401 PC ae C300 0.256 (0.069) 0.274 (0.094) 0.8868 (W) 0.9189 −1 Down 0.88678 PC ae C301 0.006 (0.018) 0.002 (0.004) 0.6852 (W) 0.9189 3.72 Up 0.68516 PC ae C302 0.051 (0.011) 0.050 (0.013) 0.4358 (W) 0.9129 1.09 Up 0.43585 PC ae C321 1.888 (0.387) 2.071 (0.446) 0.6532 (W) 0.828 −1.04 Down 0.6532 PC ae C322 0.412 (0.085) 0.443 (0.115) 0.9215 (W) 0.9129 −1.02 Down 0.92148 PC ae C340 1.158 (0.413) 1.361 (0.494) 0.2134 (W) 0.7245 −1.13 Down 0.21336 PC ae C341 6.838 (1.733) 7.685 (2.408) 0.4364 0.9074 −1.07 Down 0.43642 PC ae C342 9.013 (2.529) 9.212 (2.840) 0.7341 (W) 0.9746 1.03 Up 0.73413 PC ae C343 6.554 (2.061) 6.909 (1.932) 0.9563 0.9189 −1.01 Down 0.95631 PC ae C360 0.468 (0.081) 0.489 (0.112) 0.9271 0.9189 −1.01 Down 0.92713 PC ae C361 13.453 (4.681) 15.581 (5.774) 0.2134 (W) 0.7245 −1.1 Down 0.21336 PC ae C362 12.136 (3.814) 12.836 (4.462) 0.9586 0.9189 −1 Down 0.95859 PC ae C363 5.191 (1.378) 5.453 (1.593) 0.9507 0.9189 1.01 Up 0.95071 PC ae C364 13.570 (2.901) 14.298 (2.850) 0.9065 0.9129 −1.01 Down 0.90653 PC ae C365 7.690 (1.873) 8.233 (1.490) 0.7423 0.879 −1.03 Down 0.74228 PC ae C380 0.655 (0.243) 0.677 (0.227) 0.7842 (W) 0.9745 1.02 Up 0.78419 PC ae C382 1.181 (0.555) 0.894 (0.426) 0.0221 (W) 0.7245 1.37 Up 0.02213 PC ae C383 4.999 (1.851) 5.779 (1.971) 0.1422 (W) 0.7245 −1.09 Down 0.14215 PC ae C384 9.813 (2.634) 11.356 (2.870) 0.0405 (W) 0.7245 −1.11 Down 0.09047 PC ae C385 11.105 (1.914) 12.041 (2.232) 0.5887 0.8622 −1.04 Down 0.58867 PC ae C386 3.256 (0.824) 3.437 (0.845) 0.5176 (W) 0.9129 −1.01 Down 0.51757 PC ae C401 0.997 (0.332) 0.984 (0.273) 0.6704 0.9829 1.05 Up 0.6704 PC ae C402 1.226 (0.296) 1.338 (0.478) 0.6713 0.9129 −1.04 Down 0.67132 PC ae C403 1.273 (0.276) 1.422 (0.367) 0.4532 0.7245 −1.06 Down 0.45323 PC ae C404 1.972 (0.415) 2.288 (0.539) 0.1536 0.7245 −1.11 Down 0.15364 PC ae C405 3.228 (0.619) 3.708 (0.900) 0.1904 0.7245 −1.1 Down 0.19036 PC ae C406 2.726 (0.746) 3.147 (0.919) 0.2743 0.7245 −1.1 Down 0.27431 PC ae C420 0.735 (0.096) 0.772 (0.092) 0.8673 0.7245 −1.01 Down 0.86732 PC ae C421 0.355 (0.077) 0.396 (0.100) 0.4189 0.7245 −1.07 Down 0.41893 PC ae C422 0.373 (0.075) 0.416 (0.114) 0.4417 0.7245 −1.06 Down 0.44174 PC ae C423 0.636 (0.133) 0.708 (0.189) 0.4328 0.7245 −1.06 Down 0.43281 PC ae C424 0.745 (0.142) 0.830 (0.239) 0.4521 0.7398 −1.06 Down 0.45205 PC ae C425 1.676 (0.294) 1.898 (0.406) 0.2091 0.7245 −1.09 Down 0.20914 PC ae C443 0.097 (0.014) 0.110 (0.025) 0.2863 0.7245 −1.07 Down 0.28632 PC ae C444 0.287 (0.045) 0.341 (0.109) 0.2053 (W) 0.7245 −1.13 Down 0.20528 PC ae C445 1.181 (0.264) 1.352 (0.372) 0.2571 (W) 0.7245 −1.09 Down 0.25714 PC ae C446 0.968 (0.306) 1.014 (0.215) 0.3857 (W) 0.9189 −1.01 Down 0.38568 SM OH C141 4.490 (1.402) 4.811 (1.696) 0.8868 (W) 0.9189 −1.02 Down 0.88678 SM OH C161 2.891 (0.762) 3.179 (1.219) 0.7507 (W) 0.9511 −1.05 Down 0.75071 SM OH C221 11.656 (3.594) 12.166 (4.092) 0.9041 (W) 0.9453 −1 Down 0.90411 SM OH C222 8.762 (2.381) 9.531 (2.938) 0.6061 0.9129 −1.04 Down 0.6061 SM OH C241 1.046 (0.327) 1.133 (0.415) 0.6691 (W) 0.9189 −1.03 Down 0.6691 SM C160 106.500 (21.081) 111.465 (25.262) 0.8846 0.9129 −1.01 Down 0.88462 SM C161 15.843 (3.016) 15.894 (3.646) 0.5988 0.9745 1.04 Up 0.59877 SM C180 18.380 (4.183) 18.462 (6.535) 0.6852 (W) 0.9189 1.03 Up 0.68516 SM C181 10.944 (2.185) 11.138 (3.700) 0.7989 0.9189 1.02 Up 0.7989 SM C202 0.413 (0.095) 0.414 (0.103) 0.7674 (W) 0.9982 1.03 Up 0.7674 SM C240 17.760 (4.551) 18.454 (5.819) 0.9563 (W) 0.9746 1 Up 0.95633 SM C241 45.860 (9.098) 48.735 (12.026) 0.7599 0.9129 −1.02 Down 0.75989 SM C260 0.124 (0.032) 0.143 (0.052) 0.5912 (W) 0.7245 −1.1 Down 0.59115 SM C261 0.269 (0.061) 0.289 (0.080) 0.754  0.9129 −1.03 Down 0.75399 H1 5315.467 (1690.441) 5522.882 (1050.247) 0.5386 0.7614 −1.03 Down 0.53863 H1.1 5315.467 (1690.441) 5522.882 (1050.247) 0.5386 0.7614 −1.03 Down 0.53863 Alanine 287.550 (70.919) 298.638 (96.610) 0.9236 0.9189 1.01 Up 0.92363 Arginine 27.645 (23.076) 28.809 (26.274) 0.9913 (W) 0.9982 1.03 Up 0.99126 Asparagine 20.273 (10.938) 33.126 (10.015) 0.0021 0.0374 −1.57 Down 0.00206 Aspartic acid 20.527 (9.413) 21.719 (8.363) 0.6374 (W) 0.9129 1.02 Up 0.63744 Citrulline 26.417 (7.343) 26.376 (5.970) 0.7093 0.9982 1.04 Up 0.70934 Glutamine 445.505 (86.359) 498.468 (95.100) 0.313  0.7245 −1.07 Down 0.31298 Glutamic acid 99.755 (36.716) 93.141 (43.596) 0.2449 0.9129 1.15 Up 0.24492 Glycine 141.457 (29.459) 150.685 (28.755) 0.7676 0.8673 −1.02 Down 0.76763 Histidine 61.410 (26.653) 59.035 (26.294) 0.5612 (W) 0.9746 1.05 Up 0.56116 Isoleucine 62.187 (18.714) 66.182 (24.791) 0.6691 (W) 0.9829 −1.02 Down 0.6691 Leucine 109.205 (23.924) 118.444 (31.529) 0.6708 0.828 −1.04 Down 0.67084 Lysine 117.148 (38.711) 116.418 (30.150) 0.4726 0.9189 1.07 Up 0.47258 Methionine 24.807 (8.681) 23.532 (8.243) 0.4895 (W) 0.9129 1.11 Up 0.4895 Ornithine 48.653 (19.369) 42.959 (13.872) 0.2053 (W) 0.8593 1.19 Up 0.20528 Phenylalanine 65.265 (16.435) 66.344 (17.001) 0.7373 0.9746 1.03 Up 0.7373 Proline 171.545 (51.768) 165.574 (61.802) 0.4819 0.9453 1.08 Up 0.48193 Serine 72.725 (16.671) 74.715 (12.075) 0.8339 0.9189 1.02 Up 0.83385 Threonine 94.887 (49.068) 103.465 (33.760) 0.4103 (W) 0.7245 −1.03 Down 0.41032 Tryptophan 35.870 (9.353) 35.959 (7.606) 0.6387 0.9982 1.05 Up 0.63866 Tyrosine 43.460 (11.569) 37.150 (9.233) 0.0339 0.7245 1.21 Up 0.06393 Valine 207.080 (42.484) 206.341 (55.723) 0.5682 0.9982 1.05 Up 0.56825 Ac-Orn 1.169 (1.892) 0.978 (0.867) 0.9913 (W) 0.9189 1.19 Up 0.99126 ADMA 0.376 (0.106) 0.428 (0.105) 0.1681 (W) 0.7245 −1.11 Down 0.16812 alpha-AAA 1.153 (0.187) 1.048 (0.106) 0.0264 0.7245 1.15 Up 0.0264 Creatinine 56.743 (17.846) 56.706 (14.700) 0.6284 0.9987 1.05 Up 0.6284 DOPA 0.161 (0.013) 0.165 (0.016) 0.8447 0.9189 1.01 Up 0.84468 Dopamine 0.115 (0.281) 0.173 (0.316) 0.3737 (W) 0.8583 −1.44 Down 0.3737 Histamine 0.068 (0.012) 0.066 (0.015) 0.1547 (W) 0.7245 1.07 Up 0.15473 Kynurenine 2.194 (0.596) 2.071 (0.336) 0.4758 (W) 0.9129 1.1 Up 0.47577 Met-SO 0.508 (0.404) 0.357 (0.408) 0.1362 (W) 0.7245 1.45 Up 0.13616 Nitro-Tyr 0.202 (0.022) 0.197 (0.025) 0.308  0.9129 1.07 Up 0.30801 Putrescine 0.135 (0.041) 0.120 (0.029) 0.2763 (W) 0.8883 1.16 Up 0.27625 Sarcosine 12.290 (2.676) 12.996 (5.494) 0.6691 (W) 0.9829 −1.02 Down 0.6691 Serotonin 1.232 (0.495) 1.181 (0.490) 0.6623 0.9792 1.05 Up 0.66228 Spermidine 0.277 (0.227) 0.204 (0.040) 0.0316 (W) 0.7245 1.51 Up 0.03161 Spermine 1.324 (3.663) 0.522 (0.202) 0.1483 (W) 0.7245 2.51 Up 0.14834 t4-OH-Pro 16.407 (7.380) 16.120 (4.228) 0.9389 (W) 0.9189 1.05 Up 0.93889 Taurine 125.740 (37.343) 124.565 (35.055) 0.8868 (W) 0.9746 1.05 Up 0.88678 SDMA 0.216 (0.107) 0.248 (0.075) 0.1613 (W) 0.7245 −1.13 Down 0.16133 1-Methylhistidine 154.667 (50.069) 181.571 (48.374) 0.1751 (W) 0.7245 −1.12 Down 0.17513 2-Hydroxybutyric acid 64.977 (37.246) 68.006 (46.699) 0.7842 (W) 0.9982 −1.02 Down 0.78419 Acetic acid 13.997 (5.134) 16.035 (7.507) 0.6532 (W) 0.9129 −1.12 Down 0.6532 Betaine 41.463 (16.827) 41.106 (14.206) 0.5871 0.991 1.07 Up 0.58708 Acetoacetate 54.227 (97.636) 85.965 (104.382) 0.1193 (W) 0.7245 −1.67 Down 0.11932 Carnitine 25.397 (9.455) 26.588 (7.999) 0.8158 0.9189 1.02 Up 0.81585 Creatine 31.447 (22.327) 31.682 (19.176) 0.6532 (W) 0.9453 1.07 Up 0.6532 Dimethylamine 1.643 (0.673) 1.759 (0.924) 0.9255 0.9189 −1.01 Down 0.92547 Dimethylglycine 2.867 (1.372) 3.000 (1.343) 0.9631 0.9189 1.01 Up 0.96308 Citric acid 97.550 (33.781) 115.165 (47.955) 0.1804 0.8116 −1.14 Down 0.18042 Choline 3.530 (2.397) 2.735 (2.884) 0.2053 (W) 0.7245 1.31 Up 0.20528 Ethanol 21.760 (27.914) 50.924 (73.463) 0.1681 (W) 0.7245 −2.26 Down 0.16812 D-Glucose 3974.270 (1450.672) 4472.647 (1317.128) 0.0475 0.7614 −1.11 Down 0.07499 Glycerol 238.673 (70.880) 220.771 (64.519) 0.5761 (W) 0.9129 1.12 Up 0.57607 Fumaric acid 1.860 (0.662) 1.894 (0.673) 0.7526 0.9792 1.03 Up 0.75265 Formate 50.980 (25.335) 49.029 (29.597) 0.4489 (W) 0.9189 1.07 Up 0.44894 Hypoxanthine 0.310 (0.339) 0.177 (0.144) 0.0483 (W) 0.7245 1.88 Up 0.07827 Mannose 38.153 (25.137) 52.635 (32.662) 0.171  0.7245 −1.26 Down 0.17104 L-Lactic acid 3001.200 (1571.159) 2394.912 (1210.903) 0.1304 (W) 0.7614 1.32 Up 0.13036 Myo-inositol 62.160 (53.566) 63.818 (44.875) 0.6374 (W) 0.9453 1.04 Up 0.63744 Pyruvic acid 92.173 (68.259) 88.612 (49.830) 0.7245 0.9746 1.07 Up 0.72453 Succinate 23.917 (8.492) 27.588 (6.867) 0.2453 0.7245 −1.12 Down 0.24531 Pyroglutamic acid 51.727 (12.572) 62.388 (14.322) 0.046  0.7245 −1.14 Down 0.0846 Xanthine 1.220 (0.502) 1.300 (0.486) 0.8931 0.9189 −1.02 Down 0.89306 Urea 208.897 (101.155) 204.171 (67.970) 0.6532 (W) 0.9453 1.04 Up 0.6532 3-Hydroxybutyric acid 127.210 (294.130) 204.571 (316.922) 0.3281 (W) 0.8593 −1.75 Down 0.32807 3-Methylhistidine 70.197 (31.398) 60.194 (21.672) 0.1696 0.8116 1.21 Up 0.16961 Creatinine.1 36.427 (31.009) 34.529 (12.540) 0.5761 (W) 0.9129 1.1 Up 0.57607 Malonate 6.987 (2.627) 7.253 (2.473) 0.9676 0.9453 −1.01 Down 0.96757 Hippuric acid 3.547 (0.890) 3.576 (1.056) 0.6802 0.9829 1.04 Up 0.68025 Isovaleric acid 9.320 (6.241) 13.424 (10.315) 0.1613 (W) 0.7245 −1.41 Down 0.16133 3-Hydroxyisovaleric acid 0.737 (0.697) 0.612 (0.778) 0.4358 (W) 0.9129 1.34 Up 0.43585 Isopropyl alcohol 13.637 (29.294) 3.600 (2.320) 0.0058 (W) 0.7245 3.89 Up 0.00579 Acetone 37.537 (73.771) 38.776 (48.500) 0.1304 (W) 0.7245 −1.1 Down 0.13036 Isobutyric acid 7.903 (3.819) 7.088 (3.426) 0.3172 (W) 0.9129 1.2 Up 0.31725 Methanol 755.943 (289.816) 771.694 (301.412) 0.9738 (W) 0.9746 1.02 Up 0.97379 Propylene glycol 0.780 (0.419) 0.794 (0.396) 0.9871 0.9829 1 Up 0.9871 Dimethyl sulfone 4.317 (2.635) 3.871 (2.838) 0.3619 (W) 0.9129 1.13 Up 0.36195 Univariate analysis result for each metabolite. P-value is calculated with t-test as a default. P-value with (W) is calculated by the Wilcoxon Mann Whitney test.

TABLE 4 Results of Concussion Prediction: Epigenetic Markers only (total of 390 evaluated). SVM GLM PAM RF LDA DL AUC 95% CI 0.9433 (0.6660-1) 0.9822 (0.8020-1) 0.9528 (0.7950-1) 0.9377 (0.6660-1) 0.9889 (0.8020-1) 0.9889 (0.8030-1) SENSITIVITY 0.9000 0.9500 0.6670 0.8500 0.8000 0.9500 SPEC 0.8500 0.9050 0.9000 0.9500 0.9300 0.9120 Significant predictors in descending order of accuracy for each ML approach: SVM: cg00611535, cg20569893, cg06393885, cg24402518, cg20601481 GLM: cg05138188, cg27384352, cg24684739, cg00611535, ch.8.1136903F PAM: cg06393885, cg13528854, cg05138188, cg20601481, cg24104241 RF: cg06393885, cg24684739, cg18419977, cg07077221, cg03957481 LDA: cg06393885, cg19470964, cg06233996, cg05138188, cg24742298 DL: cg00611535, cg05138188, cg27384352, cg15531403, cg16117781

TABLE 5 Results of Concussion Prediction with Epigenetic Markers (total of 390 evaluated) plus Clinical Characteristics. SVM GLM PAM RF LDA DL AUC 95% CI 0.9722 (0.7737-1) 0.9988 (0.7975-1) 0.9988 (0.7915-1) 0.9255 (0.7213-1) 0.9955 (0.7728-1) 0.9888 (0.8095-1) SENSITIVITY 0.9000 0.8000 0.6753 0.8000 0.8670 0.9774 SPEC 0.9500 0.9800 0.9862 0.9500 0.9333 0.9500 Significant predictors in descending order of accuracy for each ML approach: SVM: SAC, cg07077221, cg19470964, cg06393885, cg20569893 GLM: cg05138188, cg27384352, SAC, cg00611535, cg24684739 PAM: SAC, cg20569893, cg20891481, cg06393885, cg24402518 RF: SAC, cg06393885, cg24104241, cg05138188, cg20569893 LDA: SAC, cg06393885, cg00611535, cg05138188, cg16882751 DL: SAC, cg05138188, cg24684739, cg00611535, Loss of consciousness

TABLE 6 Concussion Prediction Based on Combined Epigenetic (390 markers) Plus Metabolomics Markers. SVM GLM PAM RF LDA DL AUC 95% CI 0.8888 (0.7660-1) 0.9221 (0.8020-1) 0.8016 (0.6950-1) 0.9418 (0.7660-1) 0.8617 (0.7020-1) 0.9557 (0.8030-1) SENSITIVITY 0.8000 0.8500 0.7000 0.8500 0.7333 0.9000 SPEC 0.7500 0.9500 0.8500 0.9000 0.8000 0.9170 Significant predictors in descending order of accuracy for each ML approach: SVM: cg00611535, cg20569893, cg06393885, cg24402518, cg05138188 GLM: C3.1, cg05138188, cg27384352, cg24684739, cg00611535, C16.2.OH PAM: cg06393885, cg13528854, cg05138188, cg20601481, cg24104241 RF: cg06393885, cg24684739, Asparagine, cg18419977, cg07077221 LDA: cg00611535, cg20569893, cg24742298, cg06393885, cg19470964 DL: cg05138188, cg27384352, alpha.AAA, cg00611535, cg07801620

TABLE 7 Combined Epigenetic and Metabolomic Markers Plus Clinical Characteristics for Prediction of Concussion. SVM GLM PAM RF LDA DL AUC 95% CI 0.8466 (0.6737-1) 0.9933 (0.7975-1) 0.9628 (0.7915-1) 0.9222 (0.7213-1) 0.8853 (0.7728-1) 0.9704 (0.8095-1) SENSITIVITY 0.8000 0.8500 0.8753 0.8200 0.8670 0.9000 SPEC 0.9500 0.9800 0.9862 0.9000 0.9333 0.9500 Significant predictors in descending order of accuracy for each ML approach: SVM: SAC, cg06393885, cg05138188, cg24402518, cg20569893 GLM: C3.1, cg05138188, cg27384352, cg00611535, C16.2.OH PAM: cg07801620, cg16882751, cg00611535, cg00832555, cg24402518 RF: SAC, cg03957481, cg24684739, cg25997979, cg00611535 LDA: SAC, cg06393885, cg13528854, cg16882751, cg00611535 DL: SAC, cg05138188, cg07801620, cg16117781, cg27384352

TABLE 8 Combined Metabolomic Markers Plus Clinical Characteristics for Concussion Prediction. SVM GLM PAM RF LDA DL AUC 95% CI 0.7430 (0.5660-0.9200) 0.9811 (0.8020-1) 0.9789 (0.7950-1) 0.9911 (0.7660-1) 0.7464 (0.5650-0.9278) 0.9814 (0.8030-1) SENSITIVITY 0.7000 0.9250 0.9770 0.9500 0.7000 0.9380 SPEC 0.6000 0.8500 0.9100 0.9500 0.6000 0.8120 Significant predictors in descending order of accuracy for each ML approach: SVM: SAC, Asparagine, PC.aa.C24.0, alpha.AAA, Isopropyl.alcohol GLM: SAC, Loss.of.consciousness, alpha.AAA, Asparagine, lysoPC.a.C18.2 PAM: SAC, Asparagine, PC.aa.C24.0, PC.ae.C38.2, alpha.AAA RF: SAC, Asparagine, PC.aa.C24.0, Histamine, C3 LDA: SAC, PC.aa.C24.0, Asparagine, alpha.AAA, Histamine DL: SAC, Asparagine, alpha.AAA, PC.ae.C32.1, lysoPC.a.C18.2

SUPPLEMENTAL TABLE S4 Expanded epigenomic markers-pediatric concussion kit: Cases vs Controls. Log FC LOG % % FDR Fold LOG FC Methylation Methylation TargetID CHR UCSC_REFGENE_NAME LOG10p p-Val change log2(FC) Radha Cases Control AUC CI_lower CI_upper cg10464462 17 RFFL −31.352 4.44216E−32 0.196 −0.708 −0.708 3.026 15.459 0.799 0.652 0.947 cg14201424 4 MGC45800 −31.226 5.93887E−32 0.185 −0.733 −0.733 2.921 15.809 0.753 0.593 0.913 cg08596591 9 FGD3 −31.055 8.80088E−32 0.171 −0.768 −0.768 2.781 16.289 0.759 0.600 0.918 cg05767930 12 RHOF −31.006 9.87378E−32 0.196 −0.707 −0.707 3.340 17.031 0.756 0.597 0.916 cg16795830 6 LST1 −30.988 1.02775E−31 0.237 −0.625 −0.625 4.281 18.036 0.815 0.673 0.957 cg12798338 17 TMC8; TMC6 −30.985 1.03499E−31 0.229 −0.640 −0.640 4.093 17.859 0.778 0.624 0.931 cg11852643 6 TCP11 −30.945 1.13579E−31 0.158 −0.802 −0.802 2.608 16.523 0.772 0.616 0.927 cg08024887 11 CCDC88B −30.924 1.19111E−31 0.237 −0.625 −0.625 4.344 18.335 0.747 0.585 0.909 cg05651417 7 SNX10 −30.881 1.31649E−31 0.301 −0.521 −0.521 6.098 20.249 0.802 0.656 0.949 cg00249511 11 SCT −30.811 1.54523E−31 0.229 −0.639 −0.639 4.289 18.698 0.784 0.632 0.936 cg27071460 5 PTGER4 −30.809 1.55329E−31 0.181 −0.743 −0.743 2.628 14.555 0.772 0.616 0.927 cg25526061 10 ADAM8 −30.791  1.6176E−31 0.249 −0.604 −0.604 4.803 19.286 0.799 0.652 0.947 cg06233996 18 NEDD4L −30.789 1.62449E−31 0.238 −0.624 −0.624 4.517 19.007 0.892 0.782 1.000 cg23192776 21 TIAM1 −30.783 1.64984E−31 0.209 −0.679 −0.679 3.268 15.617 0.747 0.585 0.909 cg13549444 12 PTPN6 −30.775 1.68008E−31 0.226 −0.646 −0.646 4.241 18.785 0.756 0.597 0.916 cg17213699 12 LAG3 −30.690 2.04164E−31 0.225 −0.648 −0.648 4.313 19.172 0.781 0.628 0.934 cg02253612 7 STX1A −30.610 2.45427E−31 0.222 −0.653 −0.653 3.575 16.074 0.787 0.636 0.938 cg26720543 2 INPP5D −30.607  2.4696E−31 0.233 −0.632 −0.632 4.611 19.781 0.806 0.660 0.951 cg18122874 22 IL17RA −30.586 2.59595E−31 0.283 −0.549 −0.549 6.012 21.264 0.772 0.616 0.927 cg00381233 7 VOPP1 −30.578 2.64118E−31 0.347 −0.460 −0.460 7.992 23.053 0.796 0.648 0.944 cg09243716 8 DLC1 −30.556 2.77884E−31 0.317 −0.499 −0.499 7.135 22.498 0.806 0.660 0.951 cg26433975 7 COM2 −30.530 2.95218E−31 0.264 −0.578 −0.578 5.550 21.012 0.762 0.604 0.920 cg14503399 8 CPQ −30.521 3.01444E−31 0.287 −0.542 −0.542 6.250 21.747 0.824 0.685 0.963 cg02326386 19 MOBKL2A −30.494 3.20421E−31 0.306 −0.515 −0.515 6.861 22.458 0.747 0.585 0.909 cg05851250 3 ANO10 −30.470 3.38947E−31 0.313 −0.505 −0.505 7.147 22.837 0.806 0.660 0.951 cg13655635 9 NR4A3 −30.447 3.57028E−31 0.317 −0.498 −0.498 7.333 23.109 0.748 0.587 0.910 cg07498879 22 OSM −30.420 3.79848E−31 0.230 −0.639 −0.639 4.735 20.613 0.815 0.673 0.957 cg23543318 4 SPON2 −30.413 3.86033E−31 0.260 −0.585 −0.585 4.602 17.691 0.762 0.604 0.920 cg25206113 1 RORC. −30.408 3.91138E−31 0.314 −0.503 −0.503 7.291 23.217 0.762 0.604 0.920 cg07200038 16 MMP25 −30.404 3.94704E−31 0.377 −0.423 −0.423 9.659 25.600 0.750 0.589 0.911 cg24402518 10 PIK3AP1 −30.402 3.95878E−31 0.361 −0.443 −0.443 8.668 24.017 0.886 0.772 0.999 cg26754426 3 CD200R1 −30.399 3.99339E−31 0.362 −0.441 −0.441 9.073 25.033 0.769 0.612 0.925 cg23953831 1 CD101 −30.383 4.13588E−31 0.298 −0.526 −0.526 6.800 22.819 0.769 0.612 0.925 cg26668608 2 PSD4 −30.367 4.29128E−31 0.250 −0.602 −0.602 5.355 21.435 0.778 0.624 0.931 cg06086267 7 CUX1 −30.364 4.32444E−31 0.266 −0.574 −0.574 5.844 21.937 0.775 0.620 0.929 cg21455600 7 KCTD7 −30.360  4.3641E−31 0.179 −0.748 −0.748 3.504 19.612 0.765 0.608 0.923 cg01782826 1 RCSD1 −30.338 4.59035E−31 0.316 −0.500 −0.500 7.483 23.675 0.769 0.612 0.925 cg10098353 2 CD28 −30.321  4.771E−31 0.347 −0.460 −0.460 8.642 24.898 0.769 0.612 0.925 cg14999168 11 PPFIA1 −30.283  5.2131E−31 0.357 −0.447 −0.447 9.104 25.508 0.784 0.632 0.936 cg26921036 4 TEC −30.262 5.47344E−31 0.321 −0.494 −0.494 7.780 24.266 0.815 0.673 0.957 cg05601847 4 THAP9 −30.230  5.8866E−31 0.365 −0.438 −0.438 9.552 26.159 0.812 0.669 0.955 cg09762242 11 SIPA1 −30.202 6.27891E−31 0.304 −0.517 −0.517 7.310 24.025 0.753 0.593 0.913 cg18490792 16 PLCG2 −30.183 6.55642E−31 0.282 −0.549 −0.549 6.603 23.391 0.830 0.694 0.967 cg04184297 17 SLC16A3 −30.179 6.61457E−31 0.139 −0.858 −0.858 2.704 19.507 0.799 0.652 0.947 cg03343063 1 PLEKHM2 −30.136 7.31143E−31 0.294 −0.532 −0.532 5.687 19.340 0.843 0.711 0.975 cg12374834 1 LAPTM5 −30.122 7.54815E−31 0.303 −0.519 −0.519 7.397 24.422 0.756 0.597 0.916 cg09092089 7 GIMAP7 −30.100 7.94332E−31 0.299 −0.524 −0.524 7.304 24.415 0.775 0.620 0.929 cg03286855 7 RAPGEF5 −30.094 8.04544E−31 0.418 −0.379 −0.379 12.312 29.445 0.802 0.656 0.949 cg18025430 11 MS4A4A −30.060 8.70399E−31 0.341 −0.467 −0.467 8.947 26.213 0.765 0.608 0.923 cg04889100 17 ARSG −30.060 8.71941E−31 0.168 −0.775 −0.775 3.485 20.754 0.775 0.620 0.929 cg05109049 17 EVI2B; NF1 −30.056 8.78206E−31 0.352 −0.454 −0.454 9.381 26.662 0.790 0.640 0.940 cg05694921 1 PTPRU −30.055 8.81648E−31 0.362 −0.442 −0.442 9.793 27.081 0.784 0.632 0.936 cg10303411 6 FAM65B −30.009 9.79114E−31 0.231 −0.637 −0.637 5.235 22.701 0.806 0.660 0.951 cg00037681 12 FAM113B −29.967 1.07984E−30 0.253 −0.597 −0.597 5.962 23.595 0.772 0.616 0.927 cg27210998 17 GRB2 −29.943 1.14086E−30 0.353 −0.452 −0.452 8.168 23.112 0.775 0.620 0.929 cg16732780 5 CDHR2 −29.936 1.15971E−30 0.340 −0.468 −0.468 9.152 26.906 0.769 0.612 0.925 cg04104119 5 LNPEP −29.920  1.2027E−30 0.370 −0.432 −0.432 10.459 28.275 0.809 0.664 0.953 cg13329322 2 SGPP2 −29.912 1.22587E−30 0.413 −0.384 −0.384 12.562 30.411 0.756 0.597 0.916 cg26693584 16 WWOX −29.885 1.30206E−30 0.286 −0.544 −0.544 7.177 25.129 0.790 0.640 0.940 cg24742298 17 SEZ6 −29.884  1.3047E−30 0.261 −0.583 −0.583 6.357 24.313 0.892 0.782 1.000 cg23413809 2 SIX2 −29.851 1.40809E−30 0.186 −0.730 −0.730 4.135 22.221 0.796 0.648 0.944 cg12823387 1 PTPN14 −29.794 1.60559E−30 0.436 −0.361 −0.361 14.142 32.455 0.759 0.600 0.918 cg17836354 22 MKL1 −29.773 1.68483E−30 0.263 −0.580 −0.580 6.573 24.968 0.833 0.698 0.969 cg03647767 10 JMJD1C −29.755 1.75723E−30 0.274 −0.562 −0.562 6.982 25.451 0.796 0.648 0.944 cg16478871 16 XPO6 −29.751  1.7731E−30 0.386 −0.413 −0.413 11.629 30.113 0.799 0.652 0.947 cg01178680 15 ISLR2 −29.738 1.82601E−30 0.294 −0.531 −0.531 7.725 26.260 0.765 0.608 0.923 cg02147055 1 PTPRC −29.683 2.07274E−30 0.326 −0.486 −0.486 9.091 27.846 0.765 0.608 0.923 cg27531206 1 UCK2 −29.630 2.34644E−30 0.285 −0.545 −0.545 7.573 26.544 0.799 0.652 0.947 cg01967102 3 KAT2B −29.613 2.43888E−30 0.433 −0.364 −0.364 14.510 33.548 0.843 0.711 0.975 cg16373526 11 CTTN −29.587 2.58821E−30 0.418 −0.378 −0.378 11.915 28.479 0.747 0.585 0.909 cg20293777 4 HPGDS −29.475 3.35181E−30 0.313 −0.504 −0.504 6.280 20.066 0.802 0.656 0.949 cg06951565 5 GRM6 −29.414 3.85776E−30 0.375 −0.426 −0.426 9.156 24.417 0.756 0.597 0.916 cg01555705 16 CX3CL1 −29.362 4.34559E−30 0.360 −0.444 −0.444 8.334 23.173 0.772 0.616 0.927 cg16882751 1 FCMR −28.887 1.29837E−29 0.415 −0.382 −0.382 15.642 37.672 0.895 0.786 1.000 cg21115691 1 FCAMR −28.869 1.35261E−29 0.226 −0.646 −0.646 3.486 15.429 0.855 0.728 0.982 cg21720679 20 RBL1 −28.402 3.96069E−29 0.457 −0.340 −0.340 14.386 31.488 0.765 0.608 0.923 cg11085070 9 TTF1 −28.301 5.00196E−29 0.375 −0.426 −0.426 8.848 23.586 0.776 0.622 0.930 cg05021267 20 POFUT1 −28.296 5.05413E−29 0.431 −0.366 −0.366 12.327 28.605 0.775 0.620 0.929 cg16016319 1 TRIM63 −28.211  6.157E−29 0.324 −0.489 −0.489 6.484 19.982 0.790 0.640 0.940 cg22761241 1 LINC01225 −28.116 7.66047E−29 0.422 −0.374 −0.374 11.653 27.590 0.799 0.652 0.947 cg13221285 2 SH3RF3 −27.951 1.11974E−28 0.441 −0.355 −0.355 12.953 29.355 0.747 0.585 0.909 cg07077221 3 HESRG −27.902  1.252E−28 0.357 −0.447 −0.447 7.833 21.938 0.855 0.728 0.982 cg27166718 6 PLN −27.897  1.2683E−28 0.422 −0.374 −0.374 11.581 27.410 0.784 0.632 0.936 cg08288130 8 DOK2 −27.822 1.50612E−28 0.410 −0.387 −0.387 10.761 26.214 0.753 0.593 0.913 cg00231338 12 PLBD1 −27.778  1.6683E−28 0.418 −0.379 −0.379 11.222 26.859 0.806 0.660 0.951 cg20305095 2 CUL3 −27.760 1.73681E−28 0.450 −0.347 −0.347 13.547 30.107 0.784 0.632 0.936 cg21448352 2 KLHL29 −27.606 2.47599E−28 0.419 −0.378 −0.378 11.222 26.798 0.756 0.597 0.916 cg11686792 13 RASA3 −27.540 2.88503E−28 0.293 −0.533 −0.533 5.208 17.787 0.787 0.636 0.938 cg04192740 5 STK10 −27.515 3.05439E−28 0.315 −0.502 −0.502 5.975 18.975 0.809 0.664 0.953 cg15978357 4 RHOH −27.385 4.12129E−28 0.214 −0.669 −0.669 3.074 14.332 0.747 0.585 0.909 cg24954861 15 IGF1R −27.365 4.31675E−28 0.442 −0.355 −0.355 12.744 28.853 0.830 0.694 0.967 cg03656996 15 PLCB2 −27.049 8.93827E−28 0.253 −0.596 −0.596 3.974 15.685 0.775 0.620 0.929 cg06648277 10 NKX6-2 −27.006 9.86624E−28 0.244 −0.612 −0.612 3.737 15.294 0.799 0.652 0.947 cg26256223 8 OPLAH −26.973 1.06488E−27 0.261 −0.583 −0.583 4.168 15.972 0.753 0.593 0.913 cg12011642 17 MSI2 −26.833 1.46855E−27 0.453 −0.344 −0.344 13.345 29.491 0.765 0.608 0.923 cg17078116 8 NEFM −26.655 2.21457E−27 0.279 −0.554 −0.554 4.650 16.640 0.836 0.702 0.971 cg03822198 12 MAP1LC3B2 −26.653 2.22382E−27 0.226 −0.646 −0.646 3.259 14.423 0.753 0.593 0.913 cg06203744 2 KIAA2012 −26.399 3.98819E−27 0.438 −0.359 −0.359 12.077 27.573 0.781 0.628 0.934 cg12690996 1 KLHL21 −26.344 4.52576E−27 0.419 −0.378 −0.378 10.761 25.693 0.769 0.612 0.925 cg22836191 8 CHRNA6 −26.334 4.63598E−27 0.308 −0.511 −0.511 5.513 17.887 0.830 0.694 0.967 cg04385966 3 KLHL6 −26.313 4.86807E−27 0.277 −0.557 −0.557 4.537 16.356 0.821 0.681 0.961 cg00983904 12 IFFO1 −26.066 8.58091E−27 0.412 −0.385 −0.385 10.227 24.830 0.790 0.640 0.940 cg16117781 1 TNFRSF9 −26.033 9.26684E−27 0.489 −0.311 −0.311 16.060 32.847 0.806 0.660 0.951 cg19239041 20 RASSF2 −26.000  1.0008E−26 0.354 −0.451 −0.451 7.216 20.381 0.750 0.589 0.911 cg26961808 7 FLNC −25.993 1.01563E−26 0.274 −0.562 −0.562 4.402 16.047 0.775 0.620 0.929 cg23141934 19 SWSAP1 −25.970 1.07227E−26 0.372 −0.429 −0.429 8.048 21.614 0.756 0.597 0.916 cg13073773 14 GSC −25.929 1.17705E−26 0.257 −0.591 −0.591 3.914 15.257 0.778 0.624 0.931 cg09980176 19 CCDC151 −25.740 1.81845E−26 0.356 −0.448 −0.448 7.248 20.343 0.765 0.608 0.923 cg21217024 11 GRIA4 −25.634  2.3205E−26 0.265 −0.577 −0.577 4.094 15.454 0.781 0.628 0.934 cg08972954 5 ITK −25.594 2.54951E−26 0.320 −0.495 −0.495 5.771 18.052 0.778 0.624 0.931 cg24107270 7 WDR86 −25.456 3.49563E−26 0.409 −0.388 −0.388 9.851 24.074 0.750 0.589 0.911 cg23340017 10 TLX1 −25.069 8.53257E−26 0.367 −0.435 −0.435 7.568 20.597 0.759 0.600 0.918 cg10926336 10 DCLRE1A −24.961  1.0936E−25 0.474 −0.324 −0.324 14.228 29.994 0.765 0.608 0.923 cg14015044 8 TNFRSF10C −24.925 1.18898E−25 0.091 −1.043 −1.043 0.916 10.123 0.753 0.593 0.913 cg15665792 7 ELMO1 −24.617  2.4182E−25 0.489 −0.311 −0.311 15.320 31.324 0.818 0.677 0.959 cg21340223 19 LAIR1 −24.520 3.02029E−25 0.360 −0.444 −0.444 7.076 19.679 0.778 0.624 0.931 cg07223253 7 GIMAP8 −24.201 6.29657E−25 0.275 −0.561 −0.561 4.155 15.118 0.759 0.600 0.918 cg17848763 2 GYPC −24.198 6.33248E−25 0.281 −0.551 −0.551 4.332 15.392 0.765 0.608 0.923 cg17669121 12 ATP6V0A2 −24.173 6.70897E−25 0.493 −0.307 −0.307 15.430 31.298 0.772 0.616 0.927 cg12378187 3 FEZF2 −24.161 6.90954E−25 0.401 −0.397 −0.397 8.957 22.326 0.806 0.660 0.951 cg08112940 6 ADGB −23.973  1.0642E−24 0.258 −0.588 −0.588 3.708 14.349 0.753 0.593 0.913 cg08923160 16 APOB48R −23.821 1.51052E−24 0.438 −0.359 −0.359 10.983 25.096 0.790 0.640 0.940 cg19047707 2 ECEL1 −23.754 1.76233E−24 0.241 −0.617 −0.617 3.287 13.623 0.762 0.604 0.920 cg18058230 3 TNIK −23.641 2.28813E−24 0.382 −0.418 −0.418 7.825 20.492 0.759 0.600 0.918 cg03521113 12 LRMP −23.626 2.36372E−24 0.223 −0.652 −0.652 2.882 12.946 0.778 0.624 0.931 cg21696827 17 KIAA0753 −23.607 2.47093E−24 0.464 −0.333 −0.333 12.776 27.505 0.750 0.589 0.911 cg09474331 19 TTYH1 −23.586 2.59257E−24 0.217 −0.663 −0.663 2.774 12.764 0.756 0.597 0.916 cg07117012 12 AQP2 −23.356 4.40563E−24 0.439 −0.357 −0.357 10.882 24.783 0.756 0.597 0.916 cg13431464 18 PTPN2 −23.264 5.44044E−24 0.462 −0.335 −0.335 12.448 26.925 0.759 0.600 0.918 cg18150439 2 NXPH2 −23.079 8.33465E−24 0.187 −0.729 −0.729 2.182 11.678 0.753 0.593 0.913 cg22707438 11 DGKZ −23.041 9.09571E−24 0.405 −0.393 −0.393 8.750 21.631 0.759 0.600 0.918 cg24674189 13 BRCA2 −22.978 1.05213E−23 0.456 −0.341 −0.341 11.859 26.005 0.750 0.589 0.911 cg19413693 1 VAV3 −22.943 1.13911E−23 0.436 −0.361 −0.361 10.508 24.106 0.759 0.600 0.918 cg24644188 5 FYB −22.849 1.41616E−23 0.408 −0.389 −0.389 8.860 21.721 0.756 0.597 0.916 cg25769852 12 CD69 −22.845 1.43041E−23 0.424 −0.373 −0.373 9.751 22.997 0.812 0.669 0.955 cg15917517 12 APPL2 −22.839 1.44843E−23 0.479 −0.320 −0.320 13.479 28.167 0.772 0.616 0.927 cg20132609 13 DOCK9 −22.776 1.67637E−23 0.255 −0.593 −0.593 3.475 13.628 0.753 0.593 0.913 cg14104842 7 NRCAM −22.250 5.62531 E−23  0.476 −0.322 −0.322 12.995 27.282 0.781 0.628 0.934 cg20804072 17 CCL4L1 −22.245  5.6851 E−23 0.449 −0.347 −0.347 11.067 24.629 0.762 0.604 0.920 cg01636080 1 WASF2 −22.212 6.13893E−23 0.442 −0.355 −0.355 10.580 23.935 0.787 0.636 0.938 cg19071452 2 NMI −22.198 6.33629E−23 0.362 −0.441 −0.441 6.574 18.154 0.846 0.715 0.977 cg03050022 15 RAB27A −22.066 8.58867E−23 0.463 −0.334 −0.334 11.916 25.737 0.756 0.597 0.916 cg24129356 6 HLA-DMA −22.021 9.53863E−23 0.430 −0.366 −0.366 9.799 22.764 0.861 0.737 0.986 cg00832555 3 LRRC34 −21.978 1.05316E−22 0.416 −0.381 −0.381 8.969 21.566 0.867 0.745 0.989 cg12386061 3 CTDSPL −21.797 1.59598E−22 0.375 −0.426 −0.426 6.987 18.628 0.821 0.681 0.961 cg15480817 5 TMEM171 −21.768 1.70455E−22 0.269 −0.570 −0.570 3.671 13.629 0.750 0.589 0.911 cg16049690 5 BTNL9 −21.728 1.87114E−22 0.442 −0.354 −0.354 10.396 23.497 0.815 0.673 0.957 cg00347757 13 GJB2 −21.694 2.02329E−22 0.254 −0.595 −0.595 3.321 13.066 0.769 0.612 0.925 cg23724711 12 WIBG −21.679 2.09182E−22 0.349 −0.458 −0.458 5.936 17.033 0.747 0.585 0.909 cg26070834 5 POLK −21.637 2.30562E−22 0.473 −0.325 −0.325 12.436 26.284 0.769 0.612 0.925 cg23474501 10 GPR123 −21.574 2.66714E−22 0.253 −0.597 −0.597 3.284 12.972 0.790 0.640 0.940 cg22026423 10 BUB3 −21.564 2.73084E−22 0.359 −0.445 −0.445 6.278 17.502 0.846 0.715 0.977 cg26902581 2 ITGA6 −21.551 2.81476E−22 0.417 −0.380 −0.380 8.884 21.290 0.775 0.620 0.929 cg11905061 2 AGAP1 −21.522 3.00947E−22 0.492 −0.308 −0.308 13.811 28.096 0.756 0.597 0.916 cg06345712 11 MAML2 −21.490 3.23856E−22 0.454 −0.343 −0.343 11.015 24.271 0.759 0.600 0.918 cg22499994 10 CUBN −21.462 3.44865E−22 0.403 −0.394 −0.394 8.154 20.211 0.762 0.604 0.920 cg00166343 17 CRLF3 −21.459 3.47496E−22 0.488 −0.312 −0.312 13.493 27.645 0.790 0.640 0.940 cg25997979 10 ZMIZ1 −21.432 3.69783E−22 0.233 −0.633 −0.633 2.858 12.269 0.815 0.673 0.957 cg13620034 8 MSC −21.366 4.30693E−22 0.345 −0.463 −0.463 5.724 16.612 0.812 0.669 0.955 cg07801620 11 CAT −21.259 5.50739E−22 0.495 −0.306 −0.306 13.911 28.126 0.843 0.711 0.975 cg06393885 5 GALNT10 −21.039 9.13705E−22 0.366 −0.437 −0.437 6.400 17.505 0.920 0.824 1.000 cg20569893 17 FLOT2 −21.007  9.8486E−22 0.393 −0.406 −0.406 7.497 19.099 0.904 0.800 1.000 cg04869854 11 ATPGD1 −20.974 1.06167E−21 0.450 −0.346 −0.346 10.557 23.438 0.762 0.604 0.920 cg18752527 2 HECW2 −20.939 1.15181E−21 0.433 −0.364 −0.364 9.501 21.944 0.784 0.632 0.936 cg19391966 17 TNK1 −20.918 1.20881E−21 0.284 −0.546 −0.546 3.898 13.716 0.796 0.648 0.944 cg12881222 7 STEAP1B −20.830 1.47903E−21 0.440 −0.357 −0.357 9.845 22.388 0.787 0.636 0.938 cg16902746 19 PTPRS −20.798 1.59183E−21 0.393 −0.406 −0.406 7.435 18.936 0.778 0.624 0.931 cg03094728 7 AKAP9 −20.787 1.63217E−21 0.465 −0.333 −0.333 11.401 24.536 0.769 0.612 0.925 cg22407154 17 CCL4L1; −20.721 1.90004E−21 0.474 −0.324 −0.324 12.017 25.355 0.861 0.737 0.986 CCL4L2 cg16909402 1 CD48 −20.706 1.96713E−21 0.460 −0.337 −0.337 11.070 24.048 0.753 0.593 0.913 cg25563256 17 FGF11 −20.700 1.99317E−21 0.479 −0.320 −0.320 12.369 25.826 0.806 0.660 0.951 cg09142829 12 CLEC4A −20.699 2.00105E−21 0.494 −0.306 −0.306 13.522 27.381 0.769 0.612 0.925 cg11696165 12 PXN −20.661 2.18201E−21 0.485 −0.314 −0.314 12.817 26.417 0.778 0.624 0.931 cg16931416 10 INPP5A −20.618 2.40886E−21 0.290 −0.537 −0.537 3.995 13.771 0.778 0.624 0.931 cg06147863 11 SPI1 −20.566  2.7185E−21 0.474 −0.324 −0.324 11.942 25.192 0.781 0.628 0.934 cg22019177 11 DIXDC1 −20.547 2.83692E−21 0.320 −0.495 −0.495 4.784 14.939 0.750 0.589 0.911 cg11824639 1 TCTEX1D1 −20.450  3.5484E−21 0.323 −0.490 −0.490 4.855 15.014 0.762 0.604 0.920 cg12610471 10 SPAG6 −20.386 4.11033E−21 0.473 −0.326 −0.326 11.740 24.845 0.781 0.628 0.934 cg21726846 3 KALRN −20.331  4.6619E−21 0.349 −0.457 −0.457 5.634 16.136 0.750 0.589 0.911 cg19946512 9 RNF38 −20.322 4.76089E−21 0.405 −0.393 −0.393 7.828 19.335 0.821 0.681 0.961 cg07998137 3 MAGI1 −20.296 5.05621E−21 0.426 −0.370 −0.370 8.868 20.812 0.775 0.620 0.929 cg03309253 19 LTBP4 −20.197 6.35646E−21 0.304 −0.518 −0.518 4.266 14.048 0.769 0.612 0.925 cg18954401 4 PRDM8 −20.135 7.33058E−21 0.474 −0.324 −0.324 11.717 24.716 0.784 0.632 0.936 cg12795959 20 SIRPB1 −20.059 8.73007E−21 0.324 −0.490 −0.490 4.788 14.788 0.747 0.585 0.909 cg01974370 1 NBPF20 −20.036 9.20672E−21 0.341 −0.467 −0.467 5.302 15.546 0.787 0.636 0.938 cg19823803 4 KIT −20.006 9.86694E−21 0.398 −0.400 −0.400 7.426 18.644 0.824 0.685 0.963 cg23807071 7 MAD1L1 −19.957 1.10289E−20 0.382 −0.418 −0.418 6.727 17.613 0.775 0.620 0.929 cg10977770 1 ESRRG −19.841 1.44257E−20 0.483 −0.316 −0.316 12.166 25.210 0.753 0.593 0.913 cg05364588 3 MECOM −19.837 1.45702E−20 0.409 −0.388 −0.388 7.858 19.204 0.756 0.597 0.916 cg27510024 2 RFX8 −19.762 1.72808E−20 0.478 −0.320 −0.320 11.818 24.707 0.806 0.660 0.951 cg19977280 22 APOBEC3D −19.748 1.78762E−20 0.329 −0.483 −0.483 4.861 14.795 0.769 0.612 0.925 cg16514615 5 RASGEF1C −19.661 2.18228E−20 0.396 −0.402 −0.402 7.207 18.206 0.765 0.608 0.923 cg00579036 6 CMAH −19.606 2.47604E−20 0.294 −0.532 −0.532 3.916 13.335 0.765 0.608 0.923 cg27634876 14 PTGDR −19.441  3.6212E−20 0.273 −0.563 −0.563 3.432 12.554 0.775 0.620 0.929 cg12079322 2 GALNT13 −19.415 3.84937E−20 0.205 −0.689 −0.689 2.174 10.626 0.772 0.616 0.927 cg01599770 1 PDE4B −19.235 5.81819E−20 0.458 −0.339 −0.339 10.197 22.275 0.775 0.620 0.929 cg10235355 17 PITPNM3 −19.182 6.57834E−20 0.456 −0.341 −0.341 10.074 22.084 0.750 0.589 0.911 cg11753540 11 ADM −19.159 6.93513E−20 0.283 −0.549 −0.549 3.591 12.707 0.787 0.636 0.938 cg02694427 2 HOXD12 −19.060 8.71401E−20 0.467 −0.331 −0.331 10.674 22.865 0.775 0.620 0.929 cg07625529 2 HOXD10 −19.034 9.24092E−20 0.467 −0.331 −0.331 10.641 22.810 0.769 0.612 0.925 cg16441238 14 EVL −18.884 1.30735E−19 0.447 −0.350 −0.350 9.393 21.022 0.753 0.593 0.913 cg03852144 5 GLRX −18.879 1.32248E−19 0.455 −0.342 −0.342 9.858 21.668 0.772 0.616 0.927 cg21201099 17 TBX2 −18.832 1.47177E−19 0.431 −0.365 −0.365 8.548 19.817 0.756 0.597 0.916 cg00339488 4 DCHS2 −18.779 1.66422E−19 0.354 −0.451 −0.451 5.418 15.299 0.753 0.593 0.913 cg14085715 7 MACC1 −18.746 1.79412E−19 0.377 −0.423 −0.423 6.198 16.426 0.792 0.642 0.941 cg00799539 12 ASCL1 −18.741 1.81578E−19 0.424 −0.373 −0.373 8.155 19.229 0.778 0.624 0.931 cg04961466 4 NPY5R −18.632 2.33102E−19 0.362 −0.442 −0.442 5.623 15.549 0.747 0.585 0.909 cg14666113 8 GATA4 −18.613 2.43589E−19 0.288 −0.540 −0.540 3.634 12.601 0.821 0.681 0.961 cg05040232 3 GP5 −18.568 2.70631E−19 0.376 −0.424 −0.424 6.111 16.237 0.772 0.616 0.927 cg00663077 4 ZFP42 −18.476 3.33961E−19 0.461 −0.336 −0.336 10.046 21.772 0.809 0.664 0.953 cg04228935 21 RUNX1 −18.449  3.5543E−19 0.303 −0.518 −0.518 3.953 13.026 0.747 0.585 0.909 cg23906261 10 GFRA1 −18.357 4.40045E−19 0.378 −0.422 −0.422 6.118 16.174 0.781 0.628 0.934 cg06499262 16 NFAT5 −18.287 5.16126E−19 0.479 −0.320 −0.320 11.060 23.088 0.756 0.597 0.916 cg08067617 19 F2RL3 −18.211 6.14802E−19 0.372 −0.429 −0.429 5.863 15.755 0.815 0.673 0.957 cg25747192 3 RASSF1 −18.172 6.72405E−19 0.388 −0.411 −0.411 6.437 16.571 0.756 0.597 0.916 cg10143426 1 TP73; WDR8 −18.107  7.8074E−19 0.318 −0.498 −0.498 4.241 13.344 0.790 0.640 0.940 cg08218360 6 NUDT3 −17.963 1.08889E−18 0.471 −0.327 −0.327 10.382 22.034 0.836 0.702 0.971 cg19651694 11 PHOX2A −17.894 1.27784E−18 0.360 −0.443 −0.443 5.395 14.967 0.796 0.648 0.944 cg16536329 5 ZNF454 −17.874 1.33747E−18 0.478 −0.320 −0.320 10.788 22.552 0.799 0.652 0.947 cg23724557 3 ZBTB20 −17.807  1.5603E−18 0.460 −0.337 −0.337 9.650 20.966 0.778 0.624 0.931 cg19814116 1 KCNAB2 −17.626 2.36749E−18 0.286 −0.544 −0.544 3.421 11.975 0.765 0.608 0.923 cg03530294 22 APOL3 −17.445 3.59302E−18 0.351 −0.455 −0.455 4.989 14.223 0.821 0.681 0.961 cg08493294 2 DNMT3A −17.438 3.65131E−18 0.445 −0.352 −0.352 8.629 19.408 0.756 0.597 0.916 cg19677607 8 NEFM −17.343 4.54039E−18 0.248 −0.606 −0.606 2.662 10.756 0.781 0.628 0.934 cg04207218 21 ERG −17.325 4.73122E−18 0.405 −0.393 −0.393 6.792 16.778 0.759 0.600 0.918 cg14025053 8 BAALC −17.277 5.28266E−18 0.385 −0.415 −0.415 6.025 15.664 0.750 0.589 0.911 cg17608171 14 LINC01599 −17.211 6.14739E−18 0.378 −0.423 −0.423 5.765 15.268 0.772 0.616 0.927 cg24104241 1 ACTA1 −17.163 6.87272E−18 0.442 −0.354 −0.354 8.389 18.968 0.846 0.715 0.977 cg03957481 22 KLHDC7B −17.107 7.81035E−18 0.200 −0.699 −0.699 1.911 9.558 0.855 0.728 0.982 cg25720793 1 F11R −17.011 9.75525E−18 0.321 −0.493 −0.493 4.113 12.800 0.790 0.640 0.940 cg24211504 6 SIM1 −16.954 1.11055E−17 0.430 −0.367 −0.367 7.713 17.944 0.775 0.620 0.929 cg17665121 4 TBC1D1 −16.937 1.15573E−17 0.292 −0.535 −0.535 3.433 11.776 0.765 0.608 0.923 cg17527798 3 LTF −16.875 1.33291E−17 0.469 −0.328 −0.328 9.705 20.676 0.756 0.597 0.916 cg19793962 1 PIK3CD −16.765 1.71644E−17 0.479 −0.320 −0.320 10.188 21.292 0.784 0.632 0.936 cg22504849 5 FBXW11 −16.687 2.05529E−17 0.410 −0.387 −0.387 6.780 16.530 0.762 0.604 0.920 cg03307717 21 U2AF1 −16.667 2.15156E−17 0.488 −0.311 −0.311 10.753 22.018 0.843 0.711 0.975 cg00514609 11 CRYAB; −16.607 2.46986E−17 0.445 −0.351 −0.351 8.295 18.625 0.799 0.652 0.947 HSPB2 cg13631572 14 ADCY4 −16.556 2.77797E−17 0.304 −0.517 −0.517 3.636 11.953 0.849 0.719 0.978 cg18172877 5 IRX4 −16.432 3.70197E−17 0.470 −0.328 −0.328 9.513 20.237 0.756 0.597 0.916 cg10821845 8 LY6H −16.415 3.84598E−17 0.308 −0.511 −0.511 3.700 12.002 0.772 0.616 0.927 cg15762032 10 GAD2 −16.338 4.59479E−17 0.375 −0.426 −0.426 5.443 14.501 0.855 0.728 0.982 cg04379606 16 ZNF75A; −16.316 4.82615E−17 0.314 −0.503 −0.503 3.806 12.126 0.809 0.664 0.953 TIGD7 cg00897144 13 ALOX5AP −15.933  1.1661E−16 0.444 −0.353 −0.353 7.932 17.863 0.799 0.652 0.947 cg02812947 8 ADCY8 −15.904 1.24617E−16 0.339 −0.470 −0.470 4.308 12.720 0.799 0.652 0.947 cg02996471 19 S1PR4 −15.866 1.36125E−16 0.391 −0.408 −0.408 5.809 14.857 0.769 0.612 0.925 cg23677243 15 MEIS2 −15.840 1.44489E−16 0.454 −0.343 −0.343 8.375 18.440 0.809 0.664 0.953 cg10109500 3 GHSR −15.723 1.89253E−16 0.440 −0.356 −0.356 7.677 17.429 0.784 0.632 0.936 cg00225623 13 SPATA13 −15.619 2.40298E−16 0.384 −0.416 −0.416 5.501 14.332 0.824 0.685 0.963 cg23849169 6 HCG4 −15.619 2.40464E−16 0.431 −0.365 −0.365 7.228 16.765 0.772 0.616 0.927 cg25614907 5 IRX4 −15.539 2.88755E−16 0.499 −0.302 −0.302 10.734 21.529 0.775 0.620 0.929 cg18419977 11 SLC22A18 −15.459 3.47273E−16 0.404 −0.393 −0.393 6.135 15.172 0.806 0.660 0.951 cg01698105 6 LRRC16A −15.451 3.53967E−16 0.394 −0.405 −0.405 5.770 14.654 0.823 0.683 0.962 cg06085985 11 EFCAB4A −15.411 3.87875E−16 0.490 −0.310 −0.310 10.129 20.661 0.772 0.616 0.927 cg17277890 11 TRIM22 −15.258 5.52092E−16 0.404 −0.393 −0.393 6.065 15.001 0.806 0.660 0.951 cg24604213 2 SP140L −15.202 6.28074E−16 0.371 −0.431 −0.431 4.991 13.460 0.818 0.677 0.959 cg04052466 19 AMH −15.148 7.11424E−16 0.500 −0.301 −0.301 10.560 21.132 0.787 0.636 0.938 ch.3.1226245F 3 ERC2 −15.134 7.34476E−16 0.279 −0.554 −0.554 2.924 10.461 0.830 0.694 0.967 cg01729827 20 CBLN4 −15.044 9.04134E−16 0.435 −0.361 −0.361 7.164 16.459 0.750 0.589 0.911 cg14307443 4 GALNTL6 −14.839 1.44757E−15 0.254 −0.595 −0.595 2.467 9.704 0.756 0.597 0.916 cg07212778 2 C1QL2 −14.690 2.03981E−15 0.227 −0.644 −0.644 2.059 9.062 0.802 0.656 0.949 cg26723002 8 PLAG1 −14.649 2.24233E−15 0.420 −0.377 −0.377 6.411 15.263 0.747 0.585 0.909 cg04263436 6 B3GALT4 −14.645 2.26722E−15 0.269 −0.571 −0.571 2.669 9.936 0.793 0.644 0.942 cg09870066 2 CNRIP1 −14.519 3.02651E−15 0.418 −0.379 −0.379 6.281 15.033 0.775 0.620 0.929 cg06157602 5 PCDHGA8 −14.480 3.31189E−15 0.493 −0.307 −0.307 9.746 19.762 0.769 0.612 0.925 cg27599271 14 RIPK3 −14.475 3.34965E−15 0.396 −0.402 −0.402 5.544 13.986 0.827 0.689 0.965 cg03055966 11 SBF2 −14.423 3.77574E−15 0.414 −0.383 −0.383 6.109 14.758 0.747 0.585 0.909 cg26182487 5 ABLIM3 −14.410 3.89261E−15 0.333 −0.478 −0.478 3.844 11.554 0.775 0.620 0.929 cg02737619 1 SKI −14.393 4.04536E−15 0.312 −0.506 −0.506 3.403 10.915 0.747 0.585 0.909 cg04136610 5 ADAMTS16 −14.391 4.06145E−15 0.439 −0.358 −0.358 7.026 16.020 0.756 0.597 0.916 cg20779757 15 ANPEP −14.265 5.42639E−15 0.443 −0.354 −0.354 7.139 16.128 0.793 0.644 0.942 cg08159065 5 GABRB2 −14.119 7.60104E−15 0.485 −0.315 −0.315 9.052 18.679 0.747 0.585 0.909 cg03606646 6 GFOD1 −14.020 9.55987E−15 0.489 −0.311 −0.311 9.235 18.884 0.750 0.589 0.911 cg17240760 1 CD53 −14.003 9.94145E−15 0.483 −0.316 −0.316 8.906 18.434 0.796 0.648 0.944 cg25924217 17 NPTX1 −14.002  9.9582E−15 0.399 −0.399 −0.399 5.469 13.710 0.809 0.664 0.953 cg23882019 8 TNFRSF10A −13.928 1.17991E−14 0.364 −0.438 −0.438 4.479 12.291 0.759 0.600 0.918 cg13528854 4 BST1 −13.843 1.43407E−14 0.422 −0.375 −0.375 6.176 14.636 0.870 0.750 0.991 cg06675190 15 ACAN −13.839 1.44917E−14 0.299 −0.524 −0.524 3.065 10.251 0.793 0.644 0.942 cg20684253 16 PRKCB −13.810 1.54928E−14 0.408 −0.389 −0.389 5.699 13.961 0.775 0.620 0.929 cg07836142 6 ZSCAN23 −13.772 1.69033E−14 0.379 −0.421 −0.421 4.821 12.719 0.772 0.616 0.927 cg07272395 4 MGAT4D −13.762  1.7309E−14 0.426 −0.371 −0.371 6.280 14.750 0.775 0.620 0.929 cg25781595 19 GRIN2D −13.754 1.76271E−14 0.465 −0.333 −0.333 7.871 16.930 0.747 0.585 0.909 cg26405376 5 RNF44 −13.569 2.69917E−14 0.408 −0.390 −0.390 5.598 13.731 0.821 0.681 0.961 cg15596749 1 BLACAT1 −13.423 3.77171E−14 0.426 −0.371 −0.371 6.142 14.431 0.812 0.669 0.955 cg01364478 1 PRRX1 −13.365 4.31121E−14 0.491 −0.309 −0.309 8.967 18.254 0.750 0.589 0.911 cg13409544 14 ERO1A −13.353 4.43689E−14 0.387 −0.412 −0.412 4.911 12.696 0.769 0.612 0.925 cg06470626 16 CDR2 −13.142 7.20618E−14 0.486 −0.314 −0.314 8.538 17.585 0.796 0.648 0.944 cg06604289 1 PRDM2 −13.066  8.5933E−14 0.464 −0.334 −0.334 7.469 16.110 0.753 0.593 0.913 cg19934381 17 B3GNTL1 −12.944 1.13733E−13 0.441 −0.355 −0.355 6.507 14.745 0.784 0.632 0.936 cg21215767 2 EN1 −12.931 1.17226E−13 0.481 −0.317 −0.317 8.208 17.049 0.790 0.640 0.940 cg15508761 6 HIVEP2 −12.887  1.2972E−13 0.481 −0.318 −0.318 8.153 16.956 0.769 0.612 0.925 cg17579384 1 GCSAML −12.856 1.39399E−13 0.459 −0.339 −0.339 7.144 15.579 0.809 0.664 0.953 cg20601481 11 ANKRD13D −12.841  1.4423E−13 0.316 −0.500 −0.500 3.198 10.104 0.849 0.719 0.978 cg15731655 12 HOTAIR −12.800 1.58554E−13 0.414 −0.383 −0.383 5.511 13.319 0.781 0.628 0.934 cg12691994 1 RASSF5 −12.726 1.88136E−13 0.487 −0.313 −0.313 8.350 17.154 0.784 0.632 0.936 cg03887163 1 LMX1A −12.698 2.00385E−13 0.440 −0.357 −0.357 6.334 14.411 0.781 0.628 0.934 cg26309194 21 TMPRSS2 −12.638 2.30126E−13 0.488 −0.311 −0.311 8.373 17.145 0.747 0.585 0.909 cg00803453 2 SPEG −12.619 2.40505E−13 0.374 −0.427 −0.427 4.353 11.647 0.799 0.652 0.947 cg26778001 19 LILRB1 −12.613 2.44007E−13 0.432 −0.365 −0.365 6.027 13.957 0.756 0.597 0.916 cg04510788 1 GRIK3 −12.594 2.54762E−13 0.342 −0.466 −0.466 3.646 10.648 0.793 0.644 0.942 cg16927040 3 SST −12.457 3.49497E−13 0.424 −0.372 −0.372 5.711 13.462 0.836 0.702 0.971 cg27234340 17 ASGR1 −12.452 3.53071E−13 0.422 −0.375 −0.375 5.633 13.352 0.778 0.624 0.931 cg03290752 3 TBL1XR1 −12.425 3.76048E−13 0.424 −0.372 −0.372 5.698 13.431 0.824 0.685 0.963 cg13916762 6 NHSL1 −12.394 4.03382E−13 0.487 −0.312 −0.312 8.169 16.768 0.787 0.636 0.938 cg12205413 6 BACH2 −12.303 4.98124E−13 0.455 −0.342 −0.342 6.745 14.810 0.818 0.677 0.959 cg05070626 16 SYT17 −12.221 6.01618E−13 0.347 −0.460 −0.460 3.648 10.518 0.776 0.622 0.930 cg19470964 15 LINC01585 −12.200 6.30559E−13 0.479 −0.320 −0.320 7.682 16.032 0.886 0.772 0.999 cg06444282 1 VWA5B1 −12.124 7.51673E−13 0.484 −0.315 −0.315 7.863 16.242 0.796 0.648 0.944 cg12181083 8 SULF1 −12.052 8.86432E−13 0.484 −0.315 −0.315 7.800 16.127 0.849 0.719 0.978 cg14415885 1 TBX15 −12.043 9.05712E−13 0.290 −0.537 −0.537 2.618 9.013 0.802 0.656 0.949 cg17736443 6 HIST1H2AJ −11.996 1.00813E−12 0.436 −0.361 −0.361 5.890 13.524 0.775 0.620 0.929 cg02017282 17 WNT3 −11.955 1.10829E−12 0.447 −0.350 −0.350 6.253 14.000 0.747 0.585 0.909 cg07637516 10 MXI1 −11.935 1.16087E−12 0.478 −0.321 −0.321 7.481 15.650 0.753 0.593 0.913 cg21599974 4 ARHGAP24 −11.903 1.24889E−12 0.430 −0.366 −0.366 5.685 13.207 0.806 0.660 0.951 cg12751305 1 KCNN3 −11.892 1.28352E−12 0.424 −0.372 −0.372 5.485 12.930 0.762 0.604 0.920 cg00698595 11 ETS1 −11.831  1.4772E−12 0.473 −0.325 −0.325 7.223 15.258 0.833 0.698 0.969 cg20891481 3 FOXP1 −11.804 1.57149E−12 0.455 −0.342 −0.342 6.503 14.278 0.833 0.698 0.969 cg27035251 19 JAK3 −11.798 1.59087E−12 0.465 −0.332 −0.332 6.880 14.784 0.799 0.652 0.947 cg26413174 1 TTC22 −11.796  1.5995E−12 0.349 −0.458 −0.458 3.581 10.272 0.753 0.593 0.913 cg01151966 10 NRG3 −11.785 1.64223E−12 0.399 −0.399 −0.399 4.739 11.866 0.750 0.589 0.911 cg05053923 5 LINC00992 −11.781 1.65627E−12 0.395 −0.404 −0.404 4.613 11.692 0.818 0.677 0.959 cg18024479 7 TFPI2 −11.774 1.68159E−12 0.489 −0.310 −0.310 7.889 16.125 0.778 0.624 0.931 cg06460568 11 IGF2 −11.704 1.97523E−12 0.366 −0.437 −0.437 3.909 10.693 0.793 0.644 0.942 cg04167833 5 LCP2 −11.677 2.10401E−12 0.307 −0.512 −0.512 2.819 9.169 0.772 0.616 0.927 cg22796507 1 FOXE3 −11.645 2.26228E−12 0.367 −0.435 −0.435 3.928 10.698 0.747 0.585 0.909 cg19485539 10 GFRA1 −11.602 2.50302E−12 0.369 −0.433 −0.433 3.956 10.720 0.775 0.620 0.929 cg10788355 13 ELF1 −11.532 2.93979E−12 0.468 −0.330 −0.330 6.838 14.615 0.747 0.585 0.909 cg07703495 5 ADCY2 −11.358 4.38412E−12 0.458 −0.339 −0.339 6.376 13.923 0.756 0.597 0.916 cg25975712 22 FAM19A5 −11.029 9.34854E−12 0.299 −0.525 −0.525 2.568 8.595 0.772 0.616 0.927 cg05433448 3 ATP2C1 −11.019 9.58211E−12 0.417 −0.380 −0.380 4.926 11.827 0.782 0.630 0.935 cg16732077 6 FYN −11.002 9.96105E−12 0.494 −0.306 −0.306 7.622 15.434 0.762 0.604 0.920 cg21899942 14 RGS6 −10.950  1.1226E−11 0.463 −0.334 −0.334 6.365 13.738 0.753 0.593 0.913 cg00231422 16 NOL3 −10.863 1.37135E−11 0.422 −0.375 −0.375 5.006 11.874 0.762 0.604 0.920 cg23338503 5 DOCK2 −10.832 1.47167E−11 0.488 −0.312 −0.312 7.258 14.878 0.753 0.593 0.913 cg03167496 11 BDNF −10.699 2.00111E−11 0.462 −0.335 −0.335 6.204 13.417 0.750 0.589 0.911 cg04207385 6 CNR1 −10.684 2.07021E−11 0.393 −0.405 −0.405 4.231 10.752 0.750 0.589 0.911 cg18351999 11 SIGIRR −10.679 2.09497E−11 0.465 −0.333 −0.333 6.276 13.505 0.806 0.660 0.951 cg17480467 10 PAX2 −10.556 2.77998E−11 0.392 −0.407 −0.407 4.158 10.604 0.747 0.585 0.909 cg27003782 10 CPXM2 −10.355 4.41099E−11 0.414 −0.383 −0.383 4.612 11.140 0.747 0.585 0.909 cg13795063 17 RARA −10.214  6.1149E−11 0.388 −0.411 −0.411 3.960 10.205 0.787 0.636 0.938 cg05420790 12 HOXC10 −9.931 1.17153E−10 0.413 −0.384 −0.384 4.436 10.737 0.759 0.600 0.918 cg05138188 19 KIRREL2 9.919 1.20458E−10 2.007 0.303 0.303 12.775 6.364 0.870 0.750 0.991 cg25552416 17 ZFP3 −9.914  1.2184E−10 0.457 −0.340 −0.340 5.650 12.352 0.784 0.632 0.936 cg18348337 5 MCC −9.909  1.2342E−10 0.430 −0.366 −0.366 4.860 11.296 0.775 0.620 0.929 cg17929951 20 CD40 −9.821 1.50857E−10 0.396 −0.402 −0.402 4.010 10.122 0.762 0.604 0.920 cg11257728 22 PARVG −9.678 2.10096E−10 0.478 −0.321 −0.321 6.197 12.975 0.775 0.620 0.929 cg09158314 13 TNFSF13B −9.575 2.66146E−10 0.437 −0.359 −0.359 4.911 11.229 0.747 0.585 0.909 cg18801599 17 TMEM101 −9.443 3.60861E−10 0.431 −0.365 −0.365 4.701 10.895 0.802 0.656 0.949 cg05555207 12 TBX5 −9.437 3.65195E−10 0.487 −0.313 −0.313 6.386 13.120 0.772 0.616 0.927 cg20956278 22 SLC16A8 −9.166 6.81788E−10 0.435 −0.361 −0.361 4.680 10.755 0.747 0.585 0.909 cg27641628 7 TMEM140 −9.142 7.21132E−10 0.375 −0.426 −0.426 3.376 9.009 0.756 0.597 0.916 cg11572340 10 KCNIP2 −9.088 8.15716E−10 0.471 −0.327 −0.327 5.669 12.026 0.784 0.632 0.936 cg15685268 11 IGSF9B −8.997 1.00771E−09 0.459 −0.338 −0.338 5.262 11.454 0.821 0.681 0.961 cg16046444 1 NR5A2 −8.925  1.1876E−09 0.466 −0.332 −0.332 5.412 11.620 0.765 0.608 0.923 cg15444626 17 SAMD14 −8.845 1.42937E−09 0.484 −0.315 −0.315 5.946 12.284 0.799 0.652 0.947 cg17680773 16 ATF7IP2 −8.700 1.99543E−09 0.423 −0.374 −0.374 4.198 9.928 0.772 0.616 0.927 cg17336584 6 PHACTR1 −8.673 2.12568E−09 0.435 −0.361 −0.361 4.473 10.279 0.747 0.585 0.909 cg02353184 10 SFMBT2 −8.550 2.81805E−09 0.421 −0.375 −0.375 4.109 9.749 0.775 0.620 0.929 cg02970551 1 RUNX3 −8.482 3.29817E−09 0.473 −0.325 −0.325 5.401 11.414 0.772 0.616 0.927 cg13785898 16 CKLF; TK2 −8.429 3.72082E−09 0.472 −0.326 −0.326 5.325 11.293 0.787 0.636 0.938 cg21990556 11 OSBPL5 −8.391 4.06009E−09 0.443 −0.354 −0.354 4.535 10.244 0.765 0.608 0.923 cg00611535 12 RAB5B −8.381 4.16297E−09 0.482 −0.317 −0.317 5.627 11.665 0.904 0.800 1.000 cg19113326 7 POU6F2 −8.270 5.37024E−09 0.243 −0.615 −0.615 1.498 6.176 0.769 0.612 0.925 cg09965733 14 LRFN5 −8.209 6.17764E−09 0.500 −0.301 −0.301 6.086 12.183 0.796 0.648 0.944 cg06252985 11 PC −8.119 7.59572E−09 0.405 −0.393 −0.393 3.612 8.922 0.756 0.597 0.916 cg17051560 12 SOCS2 −8.090 8.13509E−09 0.419 −0.378 −0.378 3.875 9.255 0.759 0.600 0.918 cg15930596 12 KCNA1 −8.053 8.85939E−09 0.499 −0.302 −0.302 5.966 11.958 0.759 0.600 0.918 cg27134386 4 GABRA2 −8.030 9.34098E−09 0.445 −0.352 −0.352 4.423 9.947 0.769 0.612 0.925 cg05845319 3 MLH1 −7.871 1.34497E−08 0.483 −0.316 −0.316 5.350 11.081 0.809 0.664 0.953 cg09085842 6 HSPA1L −7.814 1.53597E−08 0.246 −0.609 −0.609 1.471 5.979 0.773 0.618 0.928 cg22060611 5 NRG2 −7.802 1.57662E−08 0.471 −0.327 −0.327 4.970 10.559 0.765 0.608 0.923 cg15531403 8 NPBWR1 −7.797  1.5969E−08 0.396 −0.402 −0.402 3.343 8.439 0.784 0.632 0.936 ch.10.1562909R 10 CCDC109A −7.783 1.64879E−08 0.477 −0.322 −0.322 5.122 10.747 0.790 0.640 0.940 ch.8.1136903F 8 SNTG1 −7.710 1.94795E−08 0.401 −0.397 −0.397 3.406 8.487 0.790 0.640 0.940 cg24134845 10 HPSE2 −7.365 4.31193E−08 0.386 −0.414 −0.414 3.034 7.864 0.806 0.660 0.951 cg11083422 6 GLP1R −7.191  6.4454E−08 0.404 −0.394 −0.394 3.270 8.099 0.769 0.612 0.925 cg22715094 6 HSPA1A −7.112 7.73312E−08 0.497 −0.304 −0.304 5.311 10.689 0.753 0.593 0.913 cg00091953 4 INPP4B −6.844 1.43101E−07 0.469 −0.329 −0.329 4.417 9.426 0.784 0.632 0.936 cg00157796 6 FNDC1 −6.273 5.33361E−07 0.288 −0.541 −0.541 1.610 5.591 0.799 0.652 0.947 cg11041734 7 LIMK1 −6.172 6.73342E−07 0.490 −0.309 −0.309 4.569 9.316 0.781 0.628 0.934 cg13121699 2 ZNF804A −6.028 9.38151E−07 0.421 −0.375 −0.375 3.134 7.437 0.778 0.624 0.931 cg13320626 5 ST8SIA4 −5.905  1.2438E−06 0.491 −0.309 −0.309 4.412 8.995 0.775 0.620 0.929 cg06786153 15 TBC1D2B −5.900 1.25749E−06 0.340 −0.469 −0.469 2.036 5.989 0.765 0.608 0.923 cg14065576 1 GSTM2 −5.855 1.39644E−06 0.377 −0.423 −0.423 2.452 6.499 0.753 0.593 0.913 cg16709110 11 SLC25A22 −5.395 4.02408E−06 0.455 −0.342 −0.342 3.424 7.522 0.781 0.628 0.934 cg26663686 8 LY6H −5.338  4.5937E−06 0.498 −0.303 −0.303 4.228 8.497 0.750 0.589 0.911 cg14426999 20 TOX2 −5.291 5.11798E−06 0.404 −0.393 −0.393 2.610 6.458 0.750 0.589 0.911 cg24684739 11 ZFP91 −5.159 6.93862E−06 0.468 −0.329 −0.329 3.538 7.556 0.852 0.723 0.980 cg27384352 7 CDK5; −5.089 8.15584E−06 0.496 −0.304 −0.304 4.048 8.154 0.815 0.673 0.957 SLC4A2 cg06159486 10 CACNB2 −5.088 8.16641E−06 0.390 −0.409 −0.409 2.363 6.060 0.750 0.589 0.911 cg26082690 10 KIAA1279 −4.709 1.95375E−05 0.430 −0.366 −0.366 2.735 6.353 0.778 0.624 0.931 cg01307939 7 TMEM130 −4.641 2.28342E−05 0.242 −0.617 −0.617 1.012 4.187 0.787 0.636 0.938 cg07106633 8 FABP5 −4.516 3.04945E−05 0.466 −0.331 −0.331 3.168 6.795 0.753 0.593 0.913 ch.15.814613R 15 MYO1E −4.488 3.25211E−05 0.457 −0.340 −0.340 3.014 6.593 0.748 0.587 0.910 cg16709232 17 KIF19 −4.440 3.62932E−05 0.471 −0.327 −0.327 3.206 6.806 0.818 0.677 0.959 cg18507129 3 CHST2 −4.408 3.90766E−05 0.481 −0.318 −0.318 3.353 6.971 0.750 0.589 0.911 cg13565152 9 HINT2 4.275 5.30759E−05 2.072 0.316 0.316 6.513 3.144 0.815 0.673 0.957 cg14287565 9 ARPC5L −4.206 6.22169E−05 0.463 −0.335 −0.335 2.955 6.386 0.765 0.608 0.923 cg15278646 15 OCA2 −4.167 6.80447E−05 0.346 −0.461 −0.461 1.652 4.775 0.759 0.600 0.918 cg00622863 9 PRPF4; −4.060  8.7017E−05 0.350 −0.456 −0.456 1.657 4.734 0.75 0.583 0.908 CDC26 cg11284811 1 HENMT1 −3.659 0.000219182 0.455 −0.342 −0.342 2.570 5.652 0.756 0.597 0.916 cg25643819 6 HLA-L −3.565 0.000272499 0.439 −0.357 −0.357 2.339 5.327 0.793 0.644 0.942 cg19588399 1 SYTL1 −3.245 0.00056829 0.393 −0.405 −0.405 1.760 4.476 0.784 0.632 0.936 cg14373499 2 ACTR3 −3.131 0.000740078 0.452 −0.345 −0.345 2.275 5.031 0.790 0.640 0.940 cg01185682 17 HLF −3.002 0.000994576 0.403 −0.395 −0.395 1.750 4.342 0.759 0.600 0.918 cg16862641 6 COL9A1 −2.837 0.001453983 0.481 −0.318 −0.318 2.436 5.066 0.747 0.585 0.909 cg15615160 17 TEKT1 −2.595 0.002541547 0.450 −0.347 −0.347 1.976 4.394 0.756 0.597 0.916 cg18786479 1 CD247 −2.340 0.004573041 0.487 −0.313 −0.313 2.191 4.502 0.861 0.737 0.986 cg26470340 10 ARHGAP21 1.977 0.010553676 2.070 0.316 0.316 3.871 1.870 0.752 0.591 0.912 cg13448968 2 CHPF −1.492 0.032177843 0.398 −0.400 −0.400 1.085 2.725 0.799 0.652 0.947 cg07529534 8 ANK1 −1.468 0.034049193 0.500 −0.301 −0.301 1.698 3.399 0.747 0.585 0.909 cg23729050 2 KLF7 −1.393 0.040426451 0.483 −0.316 −0.316 1.520 3.148 0.789 0.638 0.939 cg25240468 2 CCT4 −1.376 0.042088096 0.308 −0.512 −0.512 0.672 2.186 0.818 0.677 0.959 cg08528170 19 MIR24-2 −13.133 7.36464E−14 0.412 −0.386 −0.386 5.568 13.530 0.843 0.711 0.975 cg11432250 13 MIR548AS −16.028 9.38502E−17 0.484 −0.315 −0.315 10.114 20.893 0.824 0.685 0.963 cg09918657 16 MIR193B −15.075 8.42165E−16 0.399 −0.399 −0.399 5.834 14.610 0.809 0.664 0.953 cg04293733 1 MIR137 −6.989 1.02473E−07 0.350 −0.455 −0.455 2.427 6.925 0.802 0.656 0.949 cg22388260 16 MIR365-1 −30.418 3.81667E−31 0.273 −0.564 −0.564 5.969 21.855 0.787 0.636 0.938 cg08528170 19 MIR23A −13.133 7.36464E−14 0.412 −0.386 −0.386 5.568 13.530 0.843 0.711 0.975 cg08528170 19 MIR27A −13.133 7.36464E−14 0.412 −0.386 −0.386 5.568 13.530 0.843 0.711 0.975 cg24956533 15 LOC145845 −28.502 3.14447E−29 0.475 −0.323 −0.323 16.051 33.762 0.877 0.759 0.994 cg14536682 3 LOC339862 −29.321 4.77947E−30 0.457 −0.340 −0.340 14.799 32.383 0.753 0.593 0.913 cg23307878 10 LOC105376360 −29.880 1.31813E−30 0.450 −0.347 −0.347 14.710 32.683 0.765 0.608 0.923 cg12608247 2 LOC643387 −13.669 2.14417E−14 0.478 −0.321 −0.321 8.447 17.678 0.765 0.608 0.923 cg12608247 2 LOC151174 −13.669 2.14417E−14 0.478 −0.321 −0.321 8.447 17.678 0.765 0.608 0.923 cg24662666 10 LOC282997 −30.461 3.46333E−31 0.325 −0.488 −0.488 7.573 23.298 0.787 0.636 0.938 cg24603047 2 LOC101927156 −30.083 8.26506E−31 0.279 −0.554 −0.554 6.652 23.830 0.818 0.677 0.959 cg05908371 20 LOC284798 −13.384 4.12897E−14 0.439 −0.357 −0.357 6.617 15.071 0.784 0.632 0.936 cg07044757 15 LOC145845 −16.434 3.68152E−17 0.401 −0.397 −0.397 6.325 15.794 0.855 0.728 0.982 cg00037681 12 LOC100233209 −29.967 1.07984E−30 0.253 −0.597 −0.597 5.962 23.595 0.772 0.616 0.927 cg20585676 2 LOC100132215 −24.888 1.29385E−25 0.326 −0.486 −0.486 5.862 17.966 0.756 0.597 0.916 cg12309653 15 LOC145845 −30.647 2.25346E−31 0.280 −0.552 −0.552 5.848 20.868 0.784 0.632 0.936 cg26668608 2 LOC440839 −30.367 4.29128E−31 0.250 −0.602 −0.602 5.355 21.435 0.778 0.624 0.931 cg11860632 1 LOC100506022 −30.268 5.40048E−31 0.224 −0.650 −0.650 4.754 21.216 0.753 0.593 0.913 cg00764796 16 LOC100134368 −14.715 1.92945E−15 0.362 −0.441 −0.441 4.625 12.771 0.762 0.604 0.920 cg23543318 4 LOC100130872 −30.413 3.86033E−31 0.260 −0.585 −0.585 4.602 17.691 0.762 0.604 0.920 cg11724135 20 LOC284798 −18.625 2.36872E−19 0.314 −0.503 −0.503 4.239 13.508 0.765 0.608 0.923 cg12393318 5 LOC645323 −21.419  3.8141E−22 0.275 −0.561 −0.561 3.750 13.644 0.806 0.660 0.951 cg11252953 17 C17orf101 −30.323 4.75647E−31 0.320 −0.494 −0.494 7.660 23.911 0.747 0.585 0.909 cg03081691 1 C1orf55 −31.046 8.99759E−32 0.271 −0.567 −0.567 5.036 18.579 0.864 0.741 0.987 cg27166718 6 C6orf204 −27.897  1.2683E−28 0.422 −0.374 −0.374 11.581 27.410 0.784 0.632 0.936 cg11337945 5 C5orf38 −8.413 3.85929E−09 0.436 −0.361 −0.361 4.378 10.047 0.778 0.624 0.931 cg10883375 14 C14orf43 −29.801 1.58015E−30 0.311 −0.507 −0.507 6.265 20.145 0.775 0.620 0.929 cg10050900 1 C1orf200 −13.778  1.6683E−14 0.402 −0.396 −0.396 5.486 13.653 0.765 0.608 0.923 cg03846926 10 C10orf140 −4.828 1.48757E−05 0.439 −0.357 −0.357 2.910 6.623 0.759 0.600 0.918 cg14826711 1 C1orf114 −15.637  2.3089E−16 0.366 −0.437 −0.437 4.958 13.563 0.756 0.597 0.916 cg21542248 14 C14orf23 −8.390 4.07572E−09 0.331 −0.480 −0.480 2.503 7.561 0.756 0.597 0.916 cg06499647 2 C2orf40 −5.022 9.50633E−06 0.472 −0.326 −0.326 3.536 7.489 0.750 0.589 0.911 cg12860686 5 C5orf38 −17.605 2.48351E−18 0.335 −0.475 −0.475 4.590 13.694 0.775 0.620 0.929 cg14025053 8 C8orf56 −17.277 5.28266E−18 0.385 −0.415 −0.415 6.025 15.664 0.750 0.589 0.911

Metabolomics Data.

The present disclosure describes the use of metabolomics for diagnosing TBI. Metabolomics is the study of chemical processes involving metabolites, which are small molecule intermediates and products of cellular metabolism. The method described herein includes measuring metabolite levels in a biologic sample from a patient using separation methods such as liquid chromatography, high performance liquid chromatography, gas chromatography, capillary electrophoresis, and detection methods such as nuclear magnetic resonance (NMR) and mass spectrometry (MS) including direct flow MS or a combination thereof to identify and quantify the metabolites in the biological samples. While a total of 18 cases and 18 controls underwent metabolomic analysis, combined analysis was performed in an overlapping subgroup of 17 patients with concussion and 18 controls that also underwent epigenomic analysis and constitute the basis for the epigenomic analyses previously reported. Subsequently the performance of epigenomic, metabolomic, clinical and demographic analyses individually and combined were reported for these 17 concussion cases and 18 controls (Tables 4-8).

Examples of biological samples include tissue samples, body fluids, skin, hair, follicles/roots, and mucous membranes for methylation analyses. Examples of body fluids for metabolomic analyses include blood, saliva, urine, sweat, tear, breath condensate, or blood. Examples of mucous membranes include cheek scrapings, buccal scrapings, or scrapings from the tongue.

Table 3 provide the list of metabolites (small molecules). In embodiments, metabolites include one of more of C7-DC, PC aa C260, PC aa C322, PC aa C342, PC aa 362, PC aa 382, PC aa 384, asparagine, tyrosine, alpha-AAA, spermidine, D-glucose, hypoxanthine, pyroglutamic acid, or isopropyl alcohol. They were found to be more effective and statistically significant in predicting TBI, especially in combination with epigenomic data. Table 3 also provides other metabolites that contribute to the prediction of TBI, especially in combination with epigenomic data. The performance of metabolomic markers individually or combined with other predictive markers (epigenomic, demographic and clinical) for the prediction of mTBI are displayed in Tables 4-8.

In embodiments, the metabolomic data and the epigenomic data provided herein can be combined or used together to diagnose TBI. The combination of the metabolomic data and epigenomic data provide accurate detection of TBI.

Microarray.

Differential methylation can be analyzed using a microarray system. Nucleic acids can be linked to chips, such as microchips. See, for example, U.S. Pat. Nos. 5,143,854; 6,087,112; 5,215,882; 5,707,807; 5,807,522; 5,958,342; 5,994,076; 6,004,755; 6,048,695; 6,060,240; 6,090,556; and 6,040,138. Binding to nucleic acids on microarrays can be detected by scanning the microarray with a variety of laser or charge coupled device (CCD)-based scanners, and extracting features with software packages, for example, Imagene (Biodiscovery, Hawthorne, Calif.), Feature Extraction Software (Agilent), Scanalyze (Eisen, M. 1999. SCANALYZE User Manual; Stanford Univ., Stanford, Calif. Ver 2.3.2.), or GenePix (Axon Instruments). A full panel of loci would include the 412 CpG sites listed in Table 2 that have been shown individually to be potentially clinically useful tests AUC ≤0.75. An extended microarray panel consisting of multiple (as opposed to a single) significant CpG locus per gene would further enhance predictive ability and is shown in Supplemental Table 4 and is included in this study. This expanded panel includes more than one CpG marker per gene (in contrast with Table 2 that is limited to the single best performing Cpg locus per gene). The expanded panel for use in a microarray kit can be expected to further improve predictive accuracy using epigenomic markers.

Kits.

Kits for predicting and diagnosing TBI based on methylation of CpG loci on nucleic acids are described. The kits can include the components for extracting nucleic acids including DNA and RNA including mRNA from the biological sample, the components of a microarray system, and/or for analysis of the differentially methylated genomic sites.

Artificial Intelligence (AI).

One or more AI algorithms can be used in combination with the methods described herein to improve the accuracy for predicting and/or diagnosing TBI. Representative examples of AI algorithms include Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Prediction of Analysis for Microarrays (PAM), Generalized Linear Model (GLM), and deep learning (DL).

Random Forest (RF) is a supervised classification algorithm used for regression, classification and other tasks. Multiple decision tree predictive models are randomly generated in the training phase and the mode of the classes and mean prediction of the individual trees are generated as outputs. There is a direct relationship between the number of trees in the forest and the results it can get the larger the number of trees, the more accurate the result. The difference between Random Forest algorithm and the decision tree algorithm is that in Random Forest, the processes of finding the root node and splitting the feature nodes will run randomly. The decision tree is a decision support tool that uses a tree-like graph to show the possible consequences. If one inputs a training dataset with targets and features into the decision tree, it will formulate a set of rules. Overfitting is one critical problem that may make the results worse in decision trees, but for Random Forest algorithm, if there are enough trees in the forest, the classifier won't overfit the model. Another advantage is the classifier of Random Forest can handle missing values, and the last advantage is that the Random Forest classifier can be modeled for categorical values.

Support vector machine (SVM) is primarily a classifier method that performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables. Suppose some given data points each belong to one of two classes, and the goal is to decide which class a new data point will be in. In the case of support vector machines, a data point is viewed as a p-dimensional vector (a list of p numbers), and we want to know whether we can separate such points with a (p-1)-dimensional hyperplane. This is called a linear classifier. There are many hyperplanes that might classify the data. One reasonable choice as the best hyperplane is the one that represents the largest separation, or margin, between the two classes. We choose the hyperplane so that the distance from it to the nearest data point on each side is maximized. If such a hyperplane exists, it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum margin classifier or equivalently, the perceptron of optimal stability.

Linear Discriminant Analysis (LDA) is a classification method originally developed in 1936 by R. A. Fisher. It is simple, mathematically robust and often produces models whose accuracy is as good as more complex methods. LDA is based upon the concept of searching for a linear combination of variables (predictors) that best separates two classes (targets). It is closely related to analysis of variance (ANOVA) and regression analysis, which also attempt to express one dependent variable as a linear combination of other features or measurements.

Prediction Analysis for Microarrays (PAM) is a statistical technique for class prediction from gene expression data using nearest shrunken centroids. This method identifies the subsets of genes that best characterize each class.

Generalized Linear Models (GLMs) are a broad class of models that include linear regression, ANOVA, Poisson regression, log-linear models etc. But there are some limitations of GLMs, such as, linear function, e.g. can have only a linear predictor in the systematic component, and responses must be independent.

Generally classical machine learning techniques make predictions directly from a set of features that have been pre-specified by the user. However, representation learnrning techniques transform features into some intermediate representation prior to mapping them to final predictions. Deep Learnrning (DL) is a form of representation learning that uses multiple transformation steps to create very complex features. DL is widely applied in patter recognition, image processing, computer vision, and recently in bioinformatics. DL is categorized into feed-forward artificial neural networks (ANNs), which uses more than one hidden layer (y) that connects the input (x) and output layer (z) via a weight (W) matrix. The weight matrix W which is expected to minimize the difference between the input layer (x) and the output layer (z) is considered as the best one and chosen by the system to get the best results.

Treatment.

Treatment for TBI must begin as soon as possible. In embodiments, it should begin immediately following injury and/or diagnosis of TBI. The latest guidelines for treatment of mTBI in children was recently published by the CDC (Lumba-Brown A et al. Centers for Disease Control and Prevention guideline on the Diagnosis and Management of mild traumatic brain injury among children, JAMA Pediatr doi:10.1001/jamapediatrics 2018.2853). A summary of this document is presented below.

Recommendations Related to Management and Treatment.

The CDC has put forth recommendations to serve as guidelines and best practices for management and treatment of mTBI. What follows is a summary of those published guidelines. Key guideline components include patient and family education, reassurance, psychosocial and emotional support, cognitive and physical rest and aerobic treatment, post traumatic headache management and treatment, cognitive impairment, vestibulo-oculomotor dysfunction, sleep issues, and continued monitoring upon return to school.

Patient/Family Education and Reassurance.

When caring for a pediatric patient with mTBI the family and patient should be informed as to the injury and post-concussive symptoms, provided with anticipatory guidance as to treatment and recovery; for example, waming signs of more severe injury; information about nature of injury and the expected course including symptoms and timing of recovery; ongoing monitoring of symptoms; how to prevent/minimize of further injury; a plan for resumption of cognitive and physical activity and discussion of rest; issues of timing of returning to play and resumption of school work; and follow up instructions.

Patient/Cognitive/Physical Rest and Aerobic Treatment.

In close consultation with the clinician, a plan for gradual resumption of activity should be put in place if the patient is symptom free at rest. Physical activity that doesn't exacerbate symptoms has been shown to reduce post-concussive symptoms. However, it is worth noting that physical rest and reduced physical activity immediately after a mTBI promotes a more rapid recovery. Children with chronic sleep problems post mTBI should be referred to a sleep specialist. Inadequate sleep adversely affects medical conditions such as mTBI.

Chronic headaches may occur after a mTBI and the cause may be multifactorial. Monitoring of headaches is vital as evidence supports the presence of a more severe form of mTBI in a child with progressively more severe headache. Neuroimaging should be used to evaluate progressively worsening headache. While concerns about radiation exposure to a child is always legitimate, in these circumstances the risk of the TBI is felt to exceed the risk of ionizing radiation. The guidelines advised against administering a 3% hypertonic saline solution to children with mTBI for headache symptoms outside of a research setting.

Normal vestibulo-oculomotor reflex (VOR) function is vital for normal activities of daily living. Damage to the (VOR) may manifest as dizziness, blurry vision, problems maintaining balance with head movements. Vestibular rehabilitation may be of use in treating these symptoms although the evidence is limited.

Cognitive impairment or the disruption of cognitive processing may be direct result of brain injury or may be a secondary effect of other symptoms i.e. headache and fatigue. In this event nueropsychological evaluations are recommended to determine management.

Psychosocial/Emotional Support.

The health care professional should assess the availability, type of social support (emotional, informational, instrumental and appraisal) needed by the patient. Social support has been shown to promote recovery in patients with TBI, especially for those with cognitive changes post mTBI.

Timing of return to school is a crucial consideration in cases children who have suffered from concussion. Generally, the guidelines for the timing of return to school post mTBI require a team approach to decision-making which should include the family, patient, healthcare professionals, and school teams. Post-concussion symptoms, and other issues that may be interfering with academic progress should be identified and monitored. The necessity for appropriate support and modifications to the academic workload should be addressed. In the event of prolonged symptoms, the patient should be referred to healthcare professional/pediatrician who specializes in mTBI.

Posttraumatic Headache Management/Treatment.

Children with worsening or severe headache should undergo CT imaging. Non-opiod (e.g. ibuprofen, acetaminophen) to children with severe post-concussion headache. Discussions of the risk of these agents and also the possibility of rebound headaches after discontinuation from medication

Vestibulo-Oculomotor Dysfunction Management/Treatment.

Such individuals should be referred for specialist evaluation. Evidence suggest that such findings may correlate with longer duration of post-traumatic symptoms. Physical therapy may improve symptoms such as dizziness.

Sleep Management/Treatment.

Counseling on sleep related issues and how to achieve proper amounts of sleep is of paramount importance. Maintenance of a proper amount of sleep and addressing problems of disruptive sleep may require referral to a sleep specialist.

Cognitive Impairment Management/Treatment.

Cognitive impairment affecting attention, memory, learning, response speed and executive function can develop after brain trauma. These can also be a consequence of headache, fatigue etc. A search should be made to identify the etiology of cognitive dysfunction. Consultation for neuropsychological testing might be appropriate.

Summary.

Biomarker detection of mTBI/concussion as described herein can lead to the early and accurate diagnosis and thus facilitate the management objectives outlined by the CDC. Given the evidence that a significant percentage even a majority of concussion cases remain undiagnosed, accurate biomarkers is a critical necessary complement to any effective treatment strategy.

Methods disclosed herein for include predicting, diagnosing, and/or treating patients which includes mammals, for example humans. Subjects or patients in need of (in need thereof) such predicting, diagnosing, and/or treating are subjects that may have TBI and need to be diagnosed and treated.

The following exemplary embodiments and examples illustrate exemplary methods provided herein. These exemplary embodiments and examples are not intended, nor are they to be construed, as limiting the scope of the disclosure. It will be clear that the methods can be practiced otherwise than as particularly described herein. Numerous modifications and variations are possible in view of the teachings herein and, therefore, are within the scope of the disclosure.

EXEMPLARY EMBODIMENTS

The following are exemplary embodiments:

1. A method for diagnosing traumatic brain injury (TBI), wherein the method includes:

-   -   assaying a biological sample including nucleic acids to         determine a frequency or percentage methylation of cytosine at         one or more loci throughout genome; and comparing the cytosine         methylation level of the sample to one or more controls.         2. The method of embodiment 1, wherein the one or more controls         include a biological sample from normal patients (patients         without TBI) or a sample from patients diagnosed with TBI.         3. The method of embodiment 1 or 2, wherein the method further         includes obtaining a biological sample from a patient in need         thereof.         4. The method of any one of embodiments 1-3, wherein the method         further includes extracting DNA from the biological sample.         5. The method of any one of embodiments 1-4, wherein the TBI         includes chronic traumatic encephalopathy (CTE), recurrent TBI,         or mild TBI (mTBI).         6. The method of any one of embodiments 1-5, wherein the method         further comprises calculating the patients risk of TBI based on         the cytosine methylation level at different sites throughout the         genome.         7. The method of any one of embodiments 1-6, wherein the         biological sample includes body fluid or a tissue obtained from         the patient.         8. The method of any one of embodiments 1-7, wherein the         biological sample includes blood, plasma, serum, urine, buccal         swab, saliva, sputum, urine, tear, sweat, or hair.         9. The method of any one of embodiments 1-8, where in the         percentage methylation of cytosines are determined for different         combinations of loci to calculate the probability of TBI in an         individual.         10. The method of any one of embodiments 1-8, wherein the         patient is greater than 19 years old or wherein the patient is a         pediatric patient.         11. The method of any one of embodiments 1-9, wherein the         patient is a pediatric patient less than 18 years old, less than         15 years old, less than 12 years old, less than 9 years old,         less than 6 years old, less than 3 years, or less than one year         old.         12. The method of any one of embodiments 1-11, wherein the         nucleic acids include cellular nucleic acids or wherein the         nucleic acids include cell free nucleic acids.         13. The method of any one of embodiments 1-12, wherein the         nucleic acids include DNA or RNA.         14. The method of any one of embodiments 1-13, wherein the RNA         includes mRNA.         15. The method of any one of embodiments 1-14, wherein the         nucleic acids includes cell free DNA cell free extracted from         body fluid including blood, CSF and urine.         16. The method of any one of embodiments 1-15, wherein the one         or more loci include at least two genomic loci.         17. The method of any one of embodiments 1-16, wherein the one         or more loci include the loci from Table 2, Supplemental Table         S1, Supplemental Table S2, Supplemental Table S3, Supplemental         S4, Table 4, Table 5, Table 6, Table 7, or Table 8.         18. The method of any one of embodiments 1-17, wherein the one         or more loci include the loci from Table 2, Supplemental Table         S1, Supplemental Table S2, Supplemental Table S3, Supplemental         S4, Table 4, Table 5, Table 6, Table 7, or Table 8 and wherein         the one or more loci include an AUC of 0.80 or greater, 0.85 or         greater, 0.90 or greater, or 0.95 or greater.         19. The method of any one of embodiments 1-18, wherein the one         or more loci include two, three, four, five, six, seven, eight,         nine, or 10 loci.         20. The method of any one of embodiments 1-19, wherein the assay         is a bisulfite-based methylation assay or a whole genome         methylation assay.         21. The method of any one of embodiments 1-20, wherein         measurement of the frequency or percentage methylation of         cytosine nucleotides includes using single gene or whole genome         sequencing techniques.         22. The method of any one of embodiments 1-21, wherein the         sample is obtained and stored for purposes of pathological         examination.         23. The method of any one of embodiments 1-22, wherein the         sample is stored as slides, tissue blocks, or frozen.         24. A method of any one of embodiments 1-23, wherein the method         includes assaying proteins encoded by the nucleic acids         including the differentially methylated CpG loci         25. A method of any one of embodiments 1-24, wherein the method         further comprises assaying mRNA including the differentially         methylated CpG loci.         26. A method of diagnosing TBI including assaying expression of         one or more RNA transcripts in a biological sample from a         patient, wherein the one or more RNA transcripts include         transcripts that are regulated by methylation of a CpG locus         that is differentially methylated in TBI patients; and comparing         the cytosine methylation level of the patient to a control.         27. A method of diagnosing TBI including assaying the expression         of one or more proteins in a biological sample from a patient,         wherein the one or more proteins include proteins with         expression regulated by methylation of a CpG locus that is         differentially methylated in TBI patients; and comparing the         cytosine methylation level of the patient to a control.         28. The method of any one of embodiments 1-27, wherein the         method further includes using an mRNA genome-wide chip for         assaying.         29. A method of diagnosing TBI of any one of embodiments 1-28,         wherein the method further includes using metabolomic data         obtained from the patient.         30. The method of any one of embodiments 1-29, wherein the         method includes using the one or more metabolomic markers shown         in Table 3.         31. The method of embodiment 30, wherein the one or more         metabolomic markers include C7-DC, PC aa C260, PC aa C322, PC aa         C342, PC aa 362, PC aa 382, PC aa 384, asparagine, tyrosine,         alpha-AAA, spermidine, D-glucose, hypoxanthine, pyroglutamic         acid, or isopropyl alcohol.         32. A method of diagnosing TBI in a patient, wherein the method         comprises obtaining a biological sample from the patient and         detecting and measuring one or more metabolomic markers in the         sample to determine whether the patient has TBI.         33. The method of embodiment 32, wherein the detected and         measured metabolomic markers include one or more of the small         molecules shown in Table 3.         34. The method of embodiment 32 or 33, wherein the one or more         metabolomic markers include C7-DC, PC aa C260, PC aa C322, PC aa         C342, PC aa 362, PC aa 382, PC aa 384, asparagine, tyrosine,         alpha-AAA, spermidine, D-glucose, hypoxanthine, pyroglutamic         acid, or isopropyl alcohol.         35. The method of any one of embodiments 1-34, wherein         epigenomic biomarkers are combined with metabolomic markers and         or existing clinical assessment tools and clinical findings for         improved accuracy in predicting and/or diagnosing TBI.         36. The method of any one of embodiments 1-35, wherein the         method further includes using artificial intelligence         techniques.         37. The method of any one of embodiments 1-36, wherein the         method further includes using artificial intelligence and         wherein using artificial intelligence includes using one or more         of the following machine learning algorithms: Random Forest         (RF), Support Vector Machine (SVM), Linear Discriminant Analysis         (LDA), Prediction of Analysis for Microarrays (PAM), Generalized         Linear Model (GLM), or deep learning (DL).         38. The method of any one of embodiments 1-37, wherein the         method further includes treating the patient for TBI.         39. The method of any one of embodiments 1-38, wherein the         method further includes treating the patient for TBI and wherein         treating the patient includes one or more of cognitive and         physical rest and aerobic treatment, post traumatic headache         management and treatment, cognitive impairment treatment,         vestibulo-oculomotor dysfunction treatment, sleep deprivation         treatment, or continued monitoring of the patient.         40. A kit for predicting TBI of any one of embodiments 1-39,         wherein the kit includes a microarray for identifying one or         more methylated CpG loci shown in Table 2, Supplemental Table         S1, Supplemental Table S2, Supplemental Table S3, Supplemental         S4, Table 4, Table 5, Table 6, Table 7, or Table 8 and other         components.         41. The kit of embodiment 40, wherein the kit further includes a         microarray for identifying one or more methylated CpG loci of         miRNAs shown in Supplemental Table S1, ORFs shown in         Supplemental Table S2, or LOC genes of Supplemental Table S3.         42. The kit of embodiment 40 or 41, wherein the kit further         includes an array for identifying one or more proteins encoded         by the one or more nucleic acids including the differentiated         loci.

EXAMPLES Example 1

The purpose of this study was to examine DNA methylation changes in blood leucocytes to clarify the molecular mechanisms of concussion in children and to develop potential non-invasive molecular biomarkers for the detection of pediatric concussion. The age of study patients ranged from infancy </=to 1 year of age up to <15 years. A further objective was also to look at epigenomics, metabolomics and clinical predictors in different combination for the detection of concussion using Deep Learning (DL) and other Artificial Intelligence (AI) techniques. The combination of epigenomics and metabolomics have not been previously reported for the prediction of concussion.

Materials and Methods. Study Population and Sample Collection.

This prospective case-control was conducted at the pediatric emergency center of Dokuz Eylul University School of Medicine. The study was approved by the Ethics Committee of Ankara Children's Hematology and Oncology Hospital, Ankara, Turkey. After written and verbal consents were obtained from the parents, subjects were enrolled in the study and used for metabolomic and epigenomic analyses. Of these 18 cases and 18 controls were used for the epigenomic analysis and 17 cases and 18 controls with complete clinical, epigenomic and metabolomic data were used for the combined metabolomic and epigenomic analysis. The case children had closed head trauma and a subsequent CT evaluation.

The study cohort were seen in the pediatric clinic at Dokuz Eylul University School of Medicine Hospital, Ankara, Turkey. A Glasgow Coma Score (GCS) between 13-15 was used to define mTBI or concussion. Genomic DNA from whole blood was isolated using Puregene DNA Purification kits (Gentra Systems® MN, USA) according to manufacturer's protocols. The blood samples were taken from the subjects immediately at the medical visit once the clinical diagnosis was made. All subjects were of Turkish origin and gave written informed consent prior to participation in the study. As noted, IRB approval was provided by the Ethics Committee of Ankara Children's Hematology and Oncology Hospital, Ankara, Turkey and the Human Investigation Committee of Research Institute, WIlliam Beaumont Hospital, Royal Oak, Mich., USA {IRB).

Epigenomic Analysis.

Bisulfite conversion genomic DNA and Illumina HumanMethylation450 analysis. Genomic DNA (500 ng) was bisulfite converted using the EZ DNA Methylation-Direct Kit (Zymo Research, Orange, Calif.). Genome-wide DNA methylation analysis was done using the HumanMetl 1ylation450 assay (Illumine, Inc. San Diego, Calif., USA) according the manufacturer's guidance. The assay assesses 450,000 CpG loci throughout the genome and covers the enhancer regions, gene bodies, promoters and CpG islands at a single-nucleotide resolution. Fluorescently-stained BeadChips were imaged by the Illumina iScan. DNA methylation data were processed using GenomeStudio software (Ver. 2.0.3; Illumine, Inc.) applying the default settings. Data were analyzed with Illumina's Genome Studio methylation analysis package program. Detailed methodology has been previously published [20].

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation. Around 25% of Illumina 450K array probes are associated with SNPs [21]. Methylation levels of a specific locus with SNPs near or within the probe sequence may influence corresponding methylated probes [22]. Therefore, in order to avoid this potential confounding, and as suggested by Illumina, SNPs near or within the probe sequence or in the target CpG dinucleotide (i.e., within 10 bp of the CpG site) were excluded from further analysis [21,23, 24]. To avoid potential bias related to significant CpG sites on sex chromosomes, CpG probes on the X and Y chromosomes were removed from the analysis to avoid gender-specific methylation bias.

Statistical Analysis.

Methylation levels for all autosomal chromosome CpG sites were calculated as n-values of the individual CpG site. Data were normalized using the Controls Normalization Method. To avoid potential experimental confounding, various statistical modeling were used. Methylation alteration levels were computed by comparing the 3-values per individual nucleotide at each CpG site between concussion cases and controls. The p-value for methylation differences between case and normal groups at each locus was calculated [25]. Filtering criteria for p-values was set at <0.05 and <0.01 to identify the most differentiating cytosines. Subsequently, the p-values were adjusted for multiple hypotheses testing and were calculated using the Benjamini-Hochberg correction for False Discovery Rate. Receiver Operating Characteristic (ROC) and Area Under Curve (AUC) were calculated in ‘R’ computer program using built-in packages and functions (v1.6.0 R package v3.2.2). This was used for calculating AUC for the individual CpG loci.

Fold changes in methylation variation were achieved by dividing the mean n-value for the probes in each CpG site by that of the normal controls. Absolute percentage difference in methylation at each CpG locus was also calculated. Based on multiple pre-set cutoff criteria, the most significantly differentiated CpG sites were selected using the criteria of 22.0-fold increase and/or 22.0-fold decrease with Benjamini-Hochberg False Discovery Rate (FDR) p<0.01. When multiple significantly differentially methylated CpG sites were present in a single gene, selection of the CpG was resolved by considering the targets with the highest fold-change ranking and the lowest p-value. After this filtering, a threshold was set to select ROC curves based on sensitivity plotted against specificity, using multiple different n-value threshold at each CPG locus to calculate paired sensitivity and specificity values and ultimately AUC for mTBI prediction. Individual markers with AUC z0.75, significant 95% CI and FDR p-value <0.0001 were considered potentially clinically significant predictors (by themselves) of pediatric concussion and further used in the pathway analysis.

Bioinformatics Analysis.

Integrated gene ontology and pathway analysis was performed using Chilibot (www.chilibot.net) database, with gene names and keywords of interest. Ingenuity Pathway Analysis (Ingenuity Systems, www.ingenuity.com) was performed for differentially methylated genes at an FDR p-value <0.0001 to investigate potential molecular functions of the candidate biomarkers. Only genes with Entrez identifiers were used in the Pathway analysis. Biological pathways that were statistically enriched, over-represented established pathways, and molecular processes were identified.

Quantitative Pyrosequencing.

To further validate the results, and to confirm that the CHIP hybridization results are not artifacts and these CpG sites are indeed robust, we tested bisulfite-converted genomic DNA by quantitative pyrosequencing analysis. A total of 25 CpG sites were selected for validation of variable methylation by pyrosequencing. One candidate site in each of 25 genes was selected and all were in the body of the gene. DNA methylation variations were compared with the data obtained from conventional quantitative pyrosequencing. Detailed methodology was published previously [20].

Metabolomic Analysis.

Both ¹H-NMR (proton-based Nuclear Magnetic Resonance) and Liquid chromatography/Mass spectrometry/Mass Spectrometry (LC-MS-MS) metabolomic analysis were performed on the serum.

¹H-NMR Sample Preparation.

Prior to NMR analysis 300 μL of the serum samples were filtered through 3-kDa cut-off centrifuge filter units at 13,000 g for 30 min at 4° C. (Amicon Microcon YM-3; Sigma-Aldrich, St. Louis, Mo.). To 200 μl of the filtrate 25 μl of D₂0 and 21 μl of 1.75 K₂HPO₄ buffer (pH 7.2) containing 5.84 mM 2-choloro pyrimidine-5-carboxylic acid and 5.8333 mM of DSS-De (disodium-2, 2-dimethyl-2-silceptentane-5-sulphonate) were added (final pH was 7.27±0.07 for all samples). 200 μl of the solution was transferred to 3 mm NMR tubes for analysis.

¹H-NMR Analysis. Samples were analyzed in a randomized order and maintained at 4° C. using the state-of-the-art SampleJet™ (Bruker, Cambridge, Mass.) sample changer. Prior to analysis by NMR, samples were heated to room temperature over 3 min before being transferred to the magnet. All ¹H-NMR experiments were recorded at 300.0 (±0.05) K on a Bruker Avance III HD 600 MHz spectrometer (Bruker-Biospin, USA) operating at 600.13 MHz equipped with a 5 mm TCI cryoprobe using the pulse sequence as reported by Ravanbaksh et al., (2015)⁹. 256 transients were acquired across 64k data points with a spectral with of 11.964 Hz and inter-pulse delay of 5.4 s between each transient. The free induction decay signal was zero filled to 128k points prior to Fourier transformation and 0.1 Hz of line broadening was applied. All peaks were referenced to the singlet produced by the internal standard DSS-De (50.00) which was also used for the accurate quantification of all metabolites. All spectra were processed and analyzed using the Chenomx NMR Suite Professional Software package (v8.1, Chenomx Inc, Edmonton, AB).

Mass Spectra Profiling.

Targeted analysis of metabolites were carried out using p180 Absolute IDQ kit (Biocrates Life Sciences AG, Innsbruk, Austria) with a TQ-S mass spectrometer coupled to a Acquity I Class ultra-pressure liquid chromatography (UPLC) system (Waters Technologies Corporation, Milford, Mass., USA). This system enables the accurate quantification of up to 180 endogenous metabolites including amino acids, acylcamitines, biogenic amines, glycerophospholipids, sphingolipids, and sugars. Serum samples were analyzed using the protocol described in AbolutelDQ manual. Briefly, serum samples were thawed on ice, vortexed and centrifuged at 4° C. for 5 minutes at 2750 g. 10 μl of blank, 3 zero samples, 7 calibration standards and 3 quality control samples were LDAded onto the filters in the upper 96 well plate and dried under a constant stream of nitrogen using a 96 well plate positive pressure manifold (Waters Technologies Corporation, Milford, Mass., USA). Subsequently, 50 μl of the derivatization solution phenylisothiocyanate was added to each well and left at room temperature for 20 minutes. The plate was dried under nitrogen for 60 minutes, followed by the addition of 300 μl of methanol containing 5 mM ammonium acetate and shaking for 30 minutes. The extracts were filtered to a collection plate under nitrogen in the pressure manifold. The eluates were diluted with water for analysis of metabolites using UPLC-MS and diluted with running solvent for analysis by flow injection analysis (FIA)-MS. Sample registration, metabolite concentrations calculations and data export were undertaken using the Biocrates MetlDQ software.

Clinical and demographic information including the SAC (Standard Assessment of Concussion tool) score were utilized.

Artificial Intelligence (AI) Analysis.

A representative set of six artificial intelligence algorithms which have been applied for problems of data classification in bioinformatics field were selected. They include Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Prediction of Analysis for Microarrays (PAM), Generalized Linear Model (GLM), and deep learning (DL).

Software Packages Utilized.

The H2O R package (https://cran.r-project.org/web/packages/h2o/h2o.pdf, Author The H2O.ai team Maintainer Tom Kraljevic <tomk@Oxdata.com>) was used to tune the parameters of the DL model.

To get the optimal predictions for the Artificial Intelligence algorithms other than DL, the caret R package (https://cran.r-project.org/web/packages/careVcaret.pdf, Maintainer Max Kuhn <mxkuhn@gmail.com>, Dec. 10, 2017) was used to tune the parameters in the models.

The variable importance functions varimp in h2o and var/mp in caret R packages were used to rank the models features in each of the predictive algorithms.

The pROC R package was used to compute area under the curve (AUC) of a receiver-operating characteristic (ROC) curve to assess the overall performance of the models.

Modeling and Evaluation. The objective of this study is to determine the performance of different categories of markers for the prediction of concussion using different Artificial Intelligence techniques. There are five marker combinations: 1. Epigenomic markers by themselves; 2. Combined epigenomic and clinical markers; 3.Combined epigenomic, and metabolic markers; 4. Combined epigenomic, clinical and metabolic markers; and 5.Combination of metabolomic and clinical markers.

The top markers from each of the groups listed above were identified and ranked using each of the 6 Artificial Intelligence techniques: Random Forest, Support Vector Machine, Linear Discriminant Analysis, Prediction Analysis for microarrays, Generalized Linear Model, and Deep Learning.

The data was split into 80% training set and 20% testing set. While dealing with a small size of data in the machine learning applications, the 80/20 split is a commonly used one. A 10-fold cross validation was performed on the 80% training data during the model construction process and tested the model on the hold out 20% of data. To avoid sampling bias, the above splitting process was repeated ten times and calculated the average AUC on the 10 hold out test sets. In addition to AUC, sensitivity, specificity, and 95% confidence intervals were calculated for the test sets.

Feature predictors were estimated using a model-based approach. In other words, a feature was considered important if it contributed to the model performance.

Artificial Intelligence Analysis.

For the prediction of concussion, the top 390 most accurate individual epigenomic markers/genes, ranked based on the area under the receiver operating characteristics curve (AUC) and the false discovery rate p-values, were evaluated for the prediction of mTBI using AI, Machine Learning techniques. DL and five other commonly used artificial intelligence methods: RF, SVM, LDA, PAM, and GLM to identify the best combination of predictive markers. The average AUCs, sensitivity and specificity values calculated on the hold out test or validation sets were reported. Next, the same process was repeated combining the group of 20 epigenomic features (predictors) with clinical and demographic predictors including the SAC score, symptoms e.g. loss of consciousness, age of the child and gender etc. From this group of features, the optimal combination of features was used to generate the specific predictive models. These were also compared across different machine learning approaches.

As noted previously, the analysis was repeated using the top 390 epigenomic markers as a single group.

Results.

The mean (SD) age of the concussion cases was 12.71±2.70 years and for controls was 12.45±2.76 for controls. This is for the overall study population of 18 mTBI cases and 18 controls. Clinical comparison of the two groups are shown in Table 1. A total of 412 CpG targets were identified in which there were statistically significant differences (increased or decreased) in cytosine methylation levels in mTBI subjects compared with the normal samples (Table 2). All CpG methylation and corresponding genes targets had an AUC ROC ≥0.75 (FDR p-value <0.0001) for the detection of isolated mTBI. 412 protein-coding genes (Table 2), 7 microRNAs (Supplementary Table S1), 12 open reading frames (ORFs) (Supplementary Table S2), and 18 LOC genes of uncertain function (Supplementary Table S3) are aberrantly methylated and associated with mTBI.

Table 1. Comparison of Demographics and Clinical Characteristics: Cases Vs Controls.

Clinical and Demographic Data: 18 Concussion cases and 18 unaffected controls (that underwent epigenetic analysis)

Mean (SD) Cases Controls p-value Number 18 18  — Age (SD) 12.71 (2.70) 12.45 (2.76)  0.771⁺ Gender (n) Female 4 4 — Male 14 14  BMI 18.36 (3.56) 20.34 (4.31)  0.165⁺ Neuropsychiatric 2 (ADHD) — — disorder (n) History of concussion 4 0 <0.01{circumflex over ( )}  (n) Time elapse (h) after 7.66 (5.55) — Injury (SD) Type of Injury (n) Closed 2 — — Impact 8 — Penetrating 5 — Sports-related injury 6 — — Loss of consciousness 5 — <0.01⁺ Loss of memory 11 — <0.01⁺ SAC-C score* (SD) 20.0 (4.02) 27.72 (1.22) <0.01⁺ Concentration 2.05 (0.93)  3.94 (0.80) <0.01⁺ Immediate Memory 11.44 (3.09) 13.72 (0.93) <0.01⁺ Orientation 4.0 (1.13)  5 (0) <0.01⁺ Delayed Recall 2.55 (1.5)  5 (0) <0.01⁺ *SAC-C score = Concentration + Immediate Memory + Orientation + Delayed Recall ⁺t-test; {circumflex over ( )}Chi-square

Table 2 (shown above) shows differentially methylated 412 genes with Target ID, Gene ID, chromosome location, % methylation change (compared to controls), and FDR p-value for each gene identified in the analysis (shown above). CpG sites with significant individual False Detection Rate p-values indicating methylation status and area under the receiving operator characteristic curve ≥0.75 appear to have strong potential as diagnostic biomarkers for Pediatric concussion.

Supplemental Table S1 (shown above) shows differentially methylated microRNAs in mTBI.

Supplemental Table S2 (shown above) shows differentially methylated 12 ORF genes.

Supplemental Table S3 (shown above) shows differentially methylated 18 LOC genes.

Table 3 shows metabolomic results of 17 mTBI cases and 18 controls (shown above). In Table 3, metabolites, such as C7-DC, PC aa C260, PC aa C322, PC aa C342, PC aa 362, PC aa 382, PC aa 384, asparagine, tyrosine, alpha-AAA, spermidine, D-glucose, hypoxanthine, pyroglutamic acid, and isopropyl alcohol, were found to be statistically significant, while the others can still contribute to the prediction of TBI, especially in combination with epigenomic data.

AUC is an effective way to summarize the overall diagnostic accuracy of any potential test. AUC 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding. In the present analysis four individual CpG loci (4 separate genes) had an individual AUC ≥0.90, with an additional 119 individual CpG loci (119 genes) having an AUC between AUC ≥0.8 to 0.90 for mTBI detection. FIG. 1 shows the ROC curves for four representative CpG sites (and associated genes) that had AUC >0.90 (FIG. 1).

Signaling pathway analyses showed that the majority of the genes that were significantly differentially methylated were involved in multiple pathways, and most were implicated in brain function, including learning and memory pathways, and Alzheimer's disease (FIG. 2). Some of the affected pathways significantly perturbed in mTBI included G-protein coupled receptor signaling, Sphingosine-1 phosphate signaling, and Sphingosine and Sphingosine-1 phosphate metabolism. Actin Cytoskeleton Signaling and Synaptic long-term potentiation, Neurotrophin/TRK signaling, eNOS signaling, CD28 signaling in T-Helper Cells, Leukocyte extravasation signaling, Superpathway of Inositol Phosphate Compounds, Ephrin receptor signaling, Protein kinase A signaling, and Axonal guidance signaling, have all been involved in both impaired brain function, including learning and memory and also associated disorders including Alzheimer's disease. These pathway alterations may be associated with the clinical pathological features and outcome of mTBI patients.

Many of the identified genes were associated with significant dysregulation of brain function. These genes included some known to be responsible for post-concussion management (PTGER4 p=1.5, E-29), mood disorder (ADCYB, p=1.2, E-14), severe epileptic encephalopathy, seizures, cognitive, behavior (GRIN2D p=1.77, E12), pediatric bipolar disorder (HSPAIL p=1.77, E6), diminished activity of purkinje cells and disruption of motor function (INPP5A p 2.4, E19). The seven important microRNAs identified in the present study are miR-24-2, miR-548AS, miR-1938, miR-137, miR-365-1, miR-23A, and miR-27A.

Artificial Intelligence Analyses Results.

The six Machine Learning (ML)/Artificial Intelligence approaches were used to combine epigenomic, clinical and metabolomic markers.

1. Epigenomic Only Markers:

The top (based on the area under the ROC curve for each individual marker) 390 best performing individual epigenomic biomarkers was used for mTBI detection. These markers displayed excellent predictive accuracy for detection of mTBI (Table 4, shown above). Table 4 shows results of concussion prediction (total of 390 evaluated).

2 Combined Epigenomic and Clinical Markers:

The top (based on the area under the ROC curve for each individual marker) 390 best performing individual epigenomic biomarkers were combined with clinical predictors for mTBI detection. Overall addition of clinical predictors performed excellently (Table 5, shown above) but did not improve performance over epigenomics alone when considering the six AI approaches but performed just as well. Table 5 shows results of concussion prediction with epigenetic markers (total of 390 evaluated) plus clinical characteristics.

3. Combined Epigenomic, and Metabolic Markers:

The top (based on the area under the ROC curve for each individual marker) 390 best performing individual epigenomic biomarkers were combined with metabolomic markers for mTBI detection. The combination of metabolomic with genomic markers had good to excellent predictive accuracy (Table 6, shown above) depending on the ML approach used but performed less well than epigenomic markers by themselves. Table 6 shows concussion prediction based on combined epigenetic (390 markers) plus metabolomic markers.

4. Combined Epigenomic, Clinical and Metabolic Markers:

The top (based on the area under the ROC curve for each individual marker) 390 best performing individual epigenomic biomarkers were combined with metabolomic and clinical markers for mTBI detection had good to outstanding performance however this was slightly less accurate then epigenomic markers by themselves or combined with clinical predictors (Table 7, shown above). Table 7 shows combined epigenetic and metabolomic markers plus clinical characteristics for prediction of concussion.

5. Combination of Metabolomic and Clinical Markers:

The combination yielded outstanding predictive accuracy when using GLM, PAM, RF and DL approaches. These yielded areas under the ROC curves varied from 0.9789-0.9911. Using SVM and LDA the AUCs were slightly less than 0.75 (moderately accurate). (Table 8, shown above). Table 8 shows combined metabolomic markers plus clinical characteristics for concussion prediction (shown above).

Supplemental Table S4 (shown above) shows expanded epigenomic markers-pediatric concussion kit: Cases vs Controls. This table shows an expanded list of CpG loci, including more than one locus per gene that was significantly differentially methylated in concussion cases versus unaffected controls that can be used on a microarray chip for predicting the presence of a concussion.

The six Machine Learing (ML)/Artificial Intelligence approaches were used to combine multiple epigenomic markers, combine epigenomic and clinical/demographic markers and finally to combine epigenomic, clinical/demographic and metabolomic markers to achieve optimal predictive accuracy, i.e. maximal sensitivity and specificity values for concussion. Excellent to outstanding predictive accuracy was achieved using different Machine Learing approaches including Deep Learning data, the area under the ROC curve for each of the six AI techniques exceeded 0.92. Overall, higher areas under the ROC curve values were consistently achieved with DL than other Machine Learning/AI methods for the different marker combinations.

Discussion.

In recognition of the clinical importance of traumatic brain injury and the significant gaps in the understanding of this disorder, the USA Food and Drug Administration (FDA) launched the launched the Critical Pathway initiative (CPI) in 2004 to promote research collaboration for the detection and treatment of TBI. Predictive tests being considered include imaging and blood-based biomarkers. Given the complexity of the cellular mechanisms in TBI the unique potential value of a systems biology approach using high through-put genomics and proteomic approaches for understanding the disease mechanisms and for bio-marker generation is now being recognized (Feala J D, Abdulhameed M D M, Yu C et al. Systems biology approaches for discovering biomarkers for traumatic brain injury. Neurotrauma 2013; 30:1101-16). There were 449 significant CpG methylation variations (p<0.05) identified in 449 genomic regions associated with pediatric concussion, including 412 protein-coding genes, 7 microRNAs, 12 ORFs and 18 LOC genes linked to mTBI.

A large number of potential epigenomic biomarkers were identified for the detection of pediatric concussion. Diagnostic performance of individual CpG methylation sites was evaluated for the prediction of pediatric concussion. The area under the ROC curve and 95% CI was used to determining the diagnostic accuracy of the individual markers. The single best performing CpG locus per gene was considered. There were 100 CpG methylation markers with good diagnostic accuracy defined as AUC ≥0.80-0.89 for the detection of concussion. In addition, there were eight other putative epigenomic-biomarkers with excellent diagnostic accuracy defined as AUC ≥0.90-1.00. While only a single marker per gene was considered, a combination of markers within the same gene can further enhance the diagnostic accuracy of prediction. Likewise, a combination of CpG loci from different genes can also improve cumulative diagnostic accuracy over that of individual epigenomic markers.

The performance of different combinations of epigenomic, metabolomic and clinical data for the prediction of pediatric concussion was assessed. Six different Machine LearninglArtificial Intelligence techniques were used to assess the robustness of each algorithms. Consistently excellent to outstanding accuracies based on the AUCs for the prediction of concussion was achieved across the different AI techniques. Deep Learnrning consistently achieved the highest overall predictive accuracy for the prediction of concussion compared to other AI techniques. Using Deep Learning and other AI techniques, epigenomic markers by themselves had good to excellent diagnostic accuracy as determined by AUC values. The performance was further improved by the addition of clinical markers and the standard concussion assessment tool, Standard assessment of Concussion (SAC) clinical evaluation and other clinical and demographic factors (they did not all contribute to overall predictive accuracy). The predictive accuracy was high with the use of epigenomic markers and slightly improved when clinical or clinical plus metabolomic markers were used when only subgroups of epigenomic markers were considered. When a large number of epigenomic markers were considered simultaneously (top 390) the performance of these markers was excellent with minimal improvement from the consideration of clinical and psychological markers or predictions. Overall, higher areas under the ROC curve values were consistently achieved with DL over other AI methods.

To determine whether the findings that genes are epigenetically modified in TBI/concussion are merely chance findings or likely to be significant it is important to assess whether biological plausibility exists. In other words, are the genes (and related gene pathways) found to be epigenetically altered known, suspected or plausibly linked to neuronal activity, the brain dysfunction or other brain disorders that that are known or suspected to develop as a result of head injury or that are currently thought to be related to traumatic neuronal injury. A number of important genes with potential functional consequences from aberrant genome-wide DNA methylation was identified. Pathway analysis revealed differentially methylated genes in mTBI that are known to be involved in impaired brain function, including learning and memory, and Alzheimer's disease; each of these over-represented (read differentially activated) pathways found in the analyses are briefly discussed below. This gives our findings significant biological plausibility.

Genes in G-Protein Coupled Receptor Signaling.

G-protein coupled receptors (GPCRs) belong to a large family of membrane proteins with characteristic seven membrane-spanning a-helical segments. GPCRs modulate numerous cellular responses to neurotransmitters and hormones at the neuronal synapse [26] and regulate processes of electric and chemical activity at the connection between neurons [27]. Specialized G-protein-signaling pathways can exert control over the levels of cytoplasmic Ca²⁺, which is involved in excitation or inhibition at the synapse [28]. Based on the location of these GPCRs, they act as auto-receptors and regulate neurotransmission at the synapses. Approximately 80% of neurotransmitters exert their effects through interactions with GPCRs [29]. This neurochemical cascade plays a significant role during concussion [30]. PTGDR (Prostoglandin D2 Receptor), which is a GPCR gene, is expressed by astrocytes and is involved in the inflammation of brain [31]. Significant differences in PTGDR gene expression were demonstrated in the rat brain after traumatic brain injury [32]. PTGER4 (Prostaglandin EP4) is involved in the defense against neurotoxicity [33] and differential DNA methylation of this gene was identified in this study that may attenuate excitotoxic brain injury [33].

Genes in Sphingosine-1 Phosphate Signaling and Sphingosine and Sphingosine-1 Phosphate Metabolism.

Sphingosine-1-phosphate (S1P) is a bioactive lipid mediator that signals through the activation of S1P receptors and a family of G protein-coupled receptors. The receptors for S1P are expressed by both astrocytes and endothelial cells in the brain [34]. This lipid mediator participates in regulating cellular functions such as cell proliferation, cell process retraction, cell survival and migration in the central nervous system (CNS). The expression level of sphingosine genes (S1P1, S1P2, S1P3, S1P4, S1P5) alters during and after brain damage [35, 36]. Receptors of sphingosine gene (S1PR4) identified to be hypomethylated in this study, have previously been reported in association with ischemic brain injury [37]. ADCY4 (Adenyl cyclase type 4) has been identified to be involved in neuro-pathophysiological conditions [38] and we observed hypomethylation of ADCY4 in this study.

Genes in Actin Cytoskeleton Signaling and Synaptic Long-Term Potentiation.

These two pathways contain five important genes which were found to be differentially methylated in this study i.e., ADCY8, GRIN20, PLCG2, FGD3, ARHGAP24. Synapses in cortical and hippocampal neurons form at cellular protrusions called dendritic spines that are rich in actin proteins [39]. Synaptic activity of the brain regulates the morphology of dendritic spines through changes in actin polymerization. These dendritic spines can alter the long-term synaptic strength [40, 41]. The process of “synaptic long term potentiation” initiates polymerization of actin filaments on dendritic spines, leading to the stability of F-actin, regulated by calcium dependent kinases and GTPases [42, 43). The gene product of ADCY8 is adenyl cyclase, and the receptor for adenyl cyclase binds to G proteins and has a GTPase activity. In the present study, patients with concussion demonstrated significant dysregulation of the ADC YB gene, which has also been reported to play a role in human mood disorders [44].

A rare sequence variation in PLCG2, and FGD3 genes (found to be hypomethylated) have been observed in Alzheimer's disease [45, 46]. The ARHGAP24 gene has a potential role in branching and outgrowth of axons and dendrites in the brain [47]. Variations in gene methylation have been observed to be associated with intellectual disability in children. The present study identified hypomethylation on GRIN2D. Alterations of the GRIN2D gene has been reported in patients with neurological deficits including severe epileptic encephalopathy, seizures, and cognitive impairment, [48]. These clinical manifestations are seen in concussion patients [49].

Genes in Neurotrophin/TRK Signaling.

Neurotrophins are class of signaling molecules belonging to the group of homodimeric polypeptide growth factors. Neurotrophins promote neuronal development, survival, and death [50] [51] [41]. Two important genes in the neurotrophin/TRK signaling pathway that were found by us to be differentially methylated following mTBI were brain-derived neurotrophic factor (BDFN) and PIK3CD. BDNF encodes for nerve growth factor and has been extensively studied and found to be associated with multiples brain disorders including traumatic brain injury [52], bipolar disorder [53], and Alzheimer's disease [54]. The PIK3CD (Phosphatidylinositol-4, 5-Bisphosphate 3-Kinase Catalytic Subunit Delta) gene is involved in immune function and although gene expression occurs primarily in leucocytes it is also expressed in the brain. Decreased expression has been reported in traumatic brain injury [55]. This is also considered to be important gene in mTBI recovery.

Genes in eNOS (Endothelial Nitric Oxide Synthase) Signaling.

Cerebral blood now is regulated by the key signaling molecule nitric oxide (NO), under both physiological conditions and after the brain injury. Two important mechanisms are known to regulate cerebral blood now; one is autoregulation involving eNOS-generated NO and the other is neurovascular coupling, where nNOS-derived NO plays a key role [56]. One study found that post-traumatic cerebral blood now and pressure autoregulation mediation by erythropoietin depended on eNOS signaling [57]. HSPAIL, found to be differentially methylated in the study, is a heat shock protein that may play a role in regulating eNOS signaling [58]. Differential methylation of HSPA IL was also been previously found to be associated with pediatric bipolar disorder [59, a type of psychiatric disorder that may follow traumatic brain injury and concussion in certain patients [60].

Genes in CD28 Signaling in T-Helper Cells.

A number of genes found to be differentially methylated in the study have important roles in immune system inflammatory pathways, including PTPRC (CD45), HLA-DMA, NFAT5 and PTPN6. Brain injury activates crosstalk between the immune system and injured brain. For example, following an intracerebral hemorrhage there is increased T lymphocyte migration [61] and increased production of cytokines occur. The adaptive immune response plays a crucial role in resolving inflammation and promoting the process of repair in neurodegeneration. These mechanisms are deficient in neurodegenerative disorders [62]. PTPRC (CD45) has been extensively studied and is highly expressed on macrophages at the site of injury in TBI [63].

Protein tyrosine phosphatase is the product of the PTPRC gene, an important regulator of T- and B-cell receptor signaling and cytokine receptor signaling. HLA-DMA encodes a Class II transmembrane protein that is expressed in antigen presenting cells such as dendrites and B-lymphocytes and has been shown to have a role in Alzheimer's disease [64]. NFAT5 codes for a gene transcription factor involved in controlling gene transcription during an immune response. A study in mice revealed disruption of expression levels of NFAT5 following ischemic injury which affected osmolarity, an important determinant of intracranial pressure [65, 66], and the neuronal cell death process [66]. The protein transcript of the PTPN6 gene is a PTP (protein tyrosine phosphatase), a signaling molecule involved in many cellular functions such as mitosis, cell growth and differentiation. It is primarily expressed in hematopoietic cells. Altered PTPN6 expression has also been identified after traumatic brain injury in a mouse model [67]. The genes, PTPRC, HLA-DMA, NFAT5 and PTPN6 were identified to be hypomethylated in this study subjects.

Genes in Leukocyte Extravasation Signaling.

Signal-induced proliferation activating family (SIPA1) gene and IL2 inducible T-cell kinase (ITK) were hypomethylated in mTBI samples in this study. The blood-brain barrier normally provides immune protection to the brain by restricting the passage of cells and other substances in and out of the brain. While leukocyte migration is essential for normal physiology and host defense, the blood-brain barrier restricts this process [34]. Inflammation is characterized by weakened blood brain barrier function, increased reactive oxygen species and endothelial cell adhesion molecules, leukocyte recruitment, recruitment of other inflammatory cells and platelets. All these processes increase during brain injury (68). The transcription product of SIPA1 encodes signal-induced proliferation activating protein 1, which is increased in several metastatic cancers and might be involved in cell migration. The role of S/PA1 in TBI is not clear at this time. The IL2 inducible T-cell kinase (ITK) gene product has capacity to induce neuroinflammation by regulating CD4⁺ T-cell activation and trafficking [69]. An ITK mutation affecting the SH2 domain has been identified in a patient who died after ischemic brain injury [70]. We identified hypomethylation on ITK gene.

Genes in Superpathway of Inositol Phosphate Compounds.

The Inositol Polyphosphate-4-Phosphatase Type IA gene (INPP4A) codes for a family of inositol phosphates and was found to be differentially methylated in this study. Inositol phosphates play a diversified role in membrane trafficking, actin cytoskeleton maintenance, and regulation of cell survival and cell death. They also anchor to the plasma membrane proteins and help regulate osmolyte concentration, [71-73]. This is important for maintaining fluid balance in the CNS through the mobilization of intracellular calcium along with the traffic of ions such as Na⁺, K⁺, H⁺ and Cl⁻ across the plasma membrane [74,75]. Physical injuries affecting the blood brain barrier may disrupt the inflow and outflow of these molecules or ions resulting in vasogenic edema and other pathologies [76]. One study showed that a patient with a large deletion in the INPP4A gene had early-onset cerebellar atrophy and myoclonic seizures [77]. Mutations of the INPPSA gene, which was found to be differentially methylated in this study, reportedly lead to diminished activity of purkinje cells and disruption of motor function [78, 79], which is another clinical feature of concussion [80].

Genes in Ephrin Receptor Signaling.

In the early embryonic brain, neuro-epithelial cells abundantly express ephrin receptors. Null mutation of the ephrin results in the development of a larger brain size during embryogenesis, while over expression of ephrin gene signaling initiates neuro-epithelial cell apoptosis, thus indicating that ephrin is important for both apoptosis and maintenance of brain size [81]. Knox reported [82] ischemic brain injury in mice with Fyn gene mutations. FYN gene hypomethylation was observed in the present study.

Genes in Protein Kinase A Signaling.

Protein kinase cascades play key roles in cellular processes such as survival, proliferation, differentiation, growth arrest and apoptosis, during which various transcription factors are induced to modify gene expression. Mitogen-activated protein kinases (MAPKs) are serine/threonine protein kinases involved in critical cellular functions such as stress response, apoptosis and the survival of neuronal cells and altered expression of MAPKs can lead to brain damage [83]. A group of protein kinases called death-associated protein kinases have also been identified in cerebral ischemic damage. They cause cell death through the induction of processes such as excitotoxicity, autophagy, membrane blebbing and DNA fragmentation [84]. At-least 3 protein kinase signaling cascades are activated in response to CNS trauma including MAPKs, protein kinase B/Akt and glycogen synthase kinase [85]. Several genes differentially methylated in this study were found to be associated with protein kinase A signaling on pathway analysis. These included PXN, CDC26 and PDE48. PXN has been identified in multiple pathways involved in the cerebellar response to hypoxia [86]. CDC26 was found to be downregulated in a murine neuronal differentiation study and the same study identified the upregulation of the BDNF [87] PDE48, which was differentially methylated in this study, is known to play a role in brain inflammation by inducing pro-inflammatory marker TNF-α in circulating monocytes and macrophages [88]. Hypoxia, neuronal differentiation and brain inflammation are all features of traumatic brain injury and concussion [89].

Genes in Axonal Guidance Signaling.

Axonal guidance plays a key role in the brain neuronal network during fetal development and also is involved in the connectivity and repair mechanism of the brain throughout life [90]. Over- or under-expression of the axonal guidance pathway induced by trauma may hamper the healing process. Wnt genes intensely influence axon plasticity and axon pathfinding. The Wnt signaling pathway is involved in disruption of axon guidance signaling during brain injuries [91, 92]. One of the major WNT family genes, WNT3, was differentially methylated in this study, and was previously found to be differentially methylated in autism, leading to dysregulation in neurogenesis and neural developmental pathways [93].

Genes in Micro RNAs and Pediatric Concussion.

MicroRNAs (miRNAs or miRs) are small noncoding RNAs that control gene expressions at the post-transcriptional level. Approximately 66% of all mRNAs are controlled by miRNAs in the human genome [94]; the cerebrum contains approximately 70% of all known miRNAs identified [95]. Crucial developmental processes of the brain including CNS development, synapse formation and memory function involves post-transcriptional regulation by miRNAs [94, 96]. Modified action in these miRNAs hamper the downstream pathways essential for normal functioning [97]. Single miRNAs targeting multiple mRNAs (messenger RNA) as well as single mRNAs targeted by multiple miRNAs have now been shown to be involved in a number of disorders [95]. Aberrant methylation of miRNA gene promoter region has now been causatively linked to the development of neurodegenerative disorders (Chhabra 2015) hence the interest in looking specifically at the methylation of miRNA genes throughout the genome.

miR-24-2, miR-548AS, miR-1938, miR-137, miR-365-1, miR-23A, and miR-27A were identified to be differentially methylated in the pediatric concussion study. A study of traumatic brain injury linked dysregulated miRNA expression led to neuronal cell death [98). The present study found differential methylation of miR24-2, supporting findings that the downregulation of the miR-24 family was associated with neuronal apoptosis and caspase-3 activation [98]. miR-24-2 has also been identified as one of the factors involved in changes in normal brain-endothelial barrier function during neuro-inflammation [99]. miR-1938 has not been investigated in brain injury studies but has been associated with the promotion of cell proliferation in glioma patients by targeting SMAD3 [100].

miR-137 is a very important micro RNA molecule that plays a crucial role in neural development, neoplastic transformation [101], neuronal maturation [102] and has been identified in association with schizophrenia [103]. It has been found to be down-regulated in the blood of stroke patients and has been associated with post-stroke depression [104]. Further, reduced expression of miR-137 has been linked with traumatic brain injury in mice, [105] which is mirrored in the clinical population of this study. miR-23A and miR-27A were both found to be hyper-methylated in pediatric concussion patients in thisstudy. They were also identified in a traumatic brain injury study where neuronal cell death was induced (98). Again, the results of this study support these findings. It has been reported that improvement in miR-23A expression levels correlate with recovery from traumatic brain injury and reduction in cognitive impairment [106]. Overexpression of miR-27A is neuroprotective and can attenuate neurological deficits, and also attenuate traumatic brain injury-induced neuronal autophagy [107].

LOC Genes.

There were 18 genes identified and designated as “LOC” e.i. that are uncharacterized and currently not annotated i.e. published gene symbols or abbreviations for the gene name is currently not available. None of these genes have previously been associated with concussion or brain injuries, and among the 18, only two genes were identified as probable targets of brain function. LOC 100134368 has been putatively associated with the neuronal development process through its differential expression levels in umbilical cord tissue from extremely low gestational age newborns [108]. LOC645323 has been identified to have a conserved exonic structure and sequence to the mouse Visc-1 transcript. The sequence match is approximately 82% and LOC645323 is considered as orthologous to mouse Visc-1 gene [109]. Visc-1 along with Visc-2 are long non-coding RNA genes that are highly expressed in brain and may have an important biological function. Visc-1 is preferentially expressed in the developing forebrain and is co-localized with rostral and caudal intemeuron migratory streams [109]. This signifies a plausible effect of these LOC genes in the pathophysiology of concussion in children on differential methylation status. However, the detailed biological functions of these genes are yet to be identified.

ORF Genes.

An open reading frame (ORF) is a nucleic acid sequence without a stop codon in its reading frame that can be translated into a potential functional protein product [110]. A total of 12 open reading frame genes were identified as being differentially methylated in this study; among them a hypomethylated gene, C2orf40 has been previously associated with a brain-related anomaly to our knowledge. C2orf40 is a neuro-immune factor and in transgenic mice, expression of this gene results in the aggregation of neurofibrillary tangle-protein tau, responsible for the induction of senescence in the central nervous system and also is associated with tumor suppression and Alzheimer's disease. Along with these functions, C2orf40 has been identified as an inflammatory factor in Alzheimer's disease (Chhabra R. (2015) miRNA and methylation: a multifaceted liaison. Chembiochem 16, 195-203).

Conclusion.

In this study, blood based epigenomic and metabolomic biomarkers that appear to have excellent to outstanding diagnostic accuracy for the detection of concussion were identified. Regarding the epigenomic aspect of the study, we have found blood molecular markers that are associated with changes in mTBI. We have also identified genes and gene pathways that are known or can be plausibly linked to neuronal and brain function and dysfunction that have been epigenetically dysregulated. These results give biological plausibility to our findings. These markers can be used by themselves or in combination with currently used clinically parameters. Similarly, metabolomic markers by themselves or combined with psychological testing scores yield good to excellent accuracy of detection for TBI. Multiple AI techniques were used simultaneously to assess and confirm the robustness of epigenomic and metabolomic biomarkers for the detection of pediatric concussion. AI has the advantage that it can find patterns in data that might not be identified using conventional statistical analysis. AI is particularly advantageous when a large number of predictors are being simultaneously evaluated in the same patient. Finally, the accuracy of AI prediction improves the larger the number of subjects evaluated by this approach. The consequence is likely to be further improvement in accuracy of prediction when larger number of cases and controls than used in this study is evaluated in the future—such as population based screening tests e.g. for high school athletes. The high predictive accuracy observed is potentially important clinically given the fact that a significant proportion, potentially even the majority of concussions are undiagnosed particularly in vulnerable subjects such as children. In the present genome-wide methylation study, >400 CpG loci had fair to outstanding CpG accuracy for TBI detection. Also, microRNAs, ORFs and LOC genes demonstrated significant methylation variation between mTBI and control subjects. Many of these genes and gene pathways are of substantial importance in normal brain development function and have been implicated in numerous brain pathologies. Deep Learning and other AI techniques were used to achieve high diagnostic performance using epigenomics alone or combined with clinical and metabolomic data. This is the first analysis using AI and the first combining epigenomic and metabolomic for concussion prediction.

The subject matter described above is provided by way of illustration only and should not be construed as limiting. Various modifications and changes may be made to the subject matter described herein without following the example embodiments and applications illustrated and described. and without departing from the true spirit and scope of the present invention, which is set forth in the following claims.

All publications, patents and patent applications cited in this specification are incorporated herein by reference in their entireties as if each individual publication, patent or patent application were specifically and individually indicated to be incorporated by reference. While the foregoing has been described in terms of various embodiments, the skilled artisan will appreciate that various modifications, substitutions, omissions, and changes may be made without departing from the spirit thereof.

As will be understood by one of ordinary skill in the art, each embodiment disclosed herein can comprise, consist essentially of, or consist of its particular stated element, step, ingredient or component. Thus, the terms “include” or “including” should be interpreted to recite: “comprise, consist of, or consist essentially of.” The transition term “comprise” or “comprises” means includes, but is not limited to, and allows for the inclusion of unspecified elements, steps, ingredients, or components, even in major amounts. The transitional phrase “consisting of” excludes any element, step, ingredient or component not specified. The transition phrase “consisting essentially of” limits the scope of the embodiment to the specified elements, steps, ingredients or components and to those that do not materially affect the embodiment.

In addition, unless otherwise indicated, numbers expressing quantities of ingredients, constituents, reaction conditions and so forth used in the specification and claims are to be understood as being modified by the term “about.” Accordingly, unless indicated to the contrary, the numerical parameters set forth in the specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the subject matter presented herein. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the subject matter presented herein are approximations, the numerical values set forth in the specific examples are reported as precisely as possible. Any numerical values, however, inherently contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.

When further clarity is required, the term “about” has the meaning reasonably ascribed to it by a person skilled in the art when used in conjunction with a stated numerical value or range, i.e. denoting somewhat more or somewhat less than the stated value or range, to within a range of ±20% of the stated value; ±15% of the stated value; ±10% of the stated value; ±5% of the stated value; ±4% of the stated value; ±3% of the stated value; ±2% of the stated value; ±1% of the stated value; or +any percentage between 1% and 20% of the stated value.

The terms “a,” “an,” “the” and similar referents used in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.

Recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.

All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context.

The use of any and all examples, or exemplary language (e.g., “such as”) provided herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.

Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member may be referred to and claimed individually or in any combination with other members of the group or other elements found herein. It is anticipated that one or more members of a group may be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

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1. A method for diagnosing traumatic brain injury (TBI), wherein the method comprises assaying a biological sample comprising nucleic acids to determine a frequency or percentage methylation of cytosine at one or more loci throughout genome; and comparing the cytosine methylation level of the sample to cytosine methylation level of a control sample.
 2. The method of claim 1, wherein the method further comprises obtaining the biological sample from a patient in need thereof.
 3. The method of claim 2, wherein the method further comprises extracting nucleic acids from the biological sample.
 4. The method of claim 1, wherein the TBI comprises chronic traumatic encephalopathy (CTE), recurrent TBI, or mild TBI (mTBI).
 5. The method of claim 1, wherein the method further comprises calculating the patient's risk of TBI based on the cytosine methylation level at different sites throughout the genome.
 6. The method of claim 1, wherein the biological sample comprises body fluid or a tissue obtained from the patient.
 7. The method of claim 1, wherein the biological sample comprises blood, plasma, serum, cerebral spinal fluid, urine, buccal swab, saliva, sputum, urine, tear, sweat, or hair.
 8. The method of claim 2, wherein the biological sample is from a patient of greater than 19 years old or wherein the biological sample is from a pediatric patient.
 9. The method of claim 1, wherein the nucleic acids comprises cellular nucleic acids or wherein the nucleic acids comprises cell free nucleic acids.
 10. The method of claim 1, wherein the one or more loci comprise one or more loci from Table 2, Supplemental Table S1, Supplemental Table S2, Supplemental Table S3, Table 4, Table 5, Table 6, Table 7, or Table
 8. 11. The method of claim 10, wherein the one or more loci comprise at least two genomic loci from Table 2, Supplemental Table S1, Supplemental Table S2, Supplemental Table S3, Supplemental Table S4, Table 4, Table 5, Table 6, Table 7, or Table
 8. 12. The method of claim 11, wherein the one or more loci comprise an AUC of 0.75 or greater, 0.80 or greater, 0.85 or greater, 0.90 or greater, or 0.95 or greater.
 13. The method of claim 1, wherein the assay is a bisulfite-based methylation assay or a whole genome methylation assay.
 14. The method of claim 1, wherein measurement of the frequency or percentage methylation of cytosine nucleotides is obtained using single gene or whole genome sequencing techniques.
 15. A method of claim 1, wherein the method further comprises assaying proteins encoded by the nucleic acids comprising the differentially methylated CpG loci, or assaying mRNA comprising the differentially methylated CpG loci.
 16. The method of claim 1, wherein the method further comprises assaying for one or more metabolomic markers shown in Table
 3. 17. The method of claim 1, wherein the method further comprising using artificial intelligence techniques.
 18. The method of claim 17, wherein using artificial intelligence techniques comprise using one or more of the following machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Prediction of Analysis for Microarrays (PAM), Generalized Linear Model (GLM), or deep learning (DL).
 19. The method of claim 2, wherein the method further comprises treating the patient.
 20. The method of claim 20, wherein treating the patient comprises one or more of cognitive and physical rest and aerobic treatment, post traumatic headache management and treatment, cognitive impairment treatment, vestibulo-oculomotor dysfunction treatment, sleep deprivation treatment, or continued monitoring of the patient. 